UCL CENTRE FOR MEDICAL IMAGE COMPUTING

OPENDAY

Thursday June 19th 2008

Front cover picture: courtesy of Dr Dean Barratt

IntroductionIn January 2005 the Computational Imaging Sciences Group (CISG) (led by Dave Hawks, Derek Hill and David Atkinson) moved to UCL and merged with the groups of Andrew Todd-Pokropek in Medical Physics and Simon Arridge and Daniel Alexander in Computer Science to form the Centre for Medical Image Computing (CMIC). In 2007 Sebastien Ourselin joined the group as Reader in Medical Image Computing. Brian Huttons Institute of Nuclear Medicine Group became part of CMIC in 2007 creating a group of 64 researchers as of May 2008.

Our Mission: We see CMIC’s work as a key component of the translational pipeline from imaging methodology arising in mathematics, computer science, physics, chemistry and the engineering disciplines to biomedical sciences and healthcare. Close working with our clinical colleagues not only provides us with the means to translate new ideas in computation, informatics and imaging into the clinic, but also creates the environment in which new questions are asked and new problems posed that we can feed back into the development of fundamental new methodology. We are building a matrix structure with orthogonal streams of fundamental methodological development and application orientated research. This allows methodology to feed into many applications, each of which can draw on a wide range of methodological expertise. Our close links with industry, including large multinational imaging companies, the pharmaceutical industry and a vibrant community of local SMEs, including our start-up IXICO, allows us to create solutions that can be widely disseminated in healthcare and bio-science.

Fund raising: Our first three years at UCL were dominated by successful efforts to secure short and medium term funding for the group. Recently we have been awarded 4 grants in breast cancer (1 large EPSRC project with Oxford, Manchester and ICR; a TSB grant with Carestream – previously Kodak, a TSB grant with Dexella on digital tomosynthesis, and an EU grant as part of the Visual Physiological Human programme (VPH)). We are building our activity in the treatment of liver metastases through a major investment by EPSRC in High Intensity Focussed Ultrasound (with Mechanical Engineering (PI Nader Saffiri) at UCL, ICR and Oxford) and an EU grant as part of the VPH programme. We are about to start a substantial project in colon cancer with industrial and NIHR funding. We have continued to raise funds for collaborations with the Institute of Cancer Research and with Oxford building on the successful MIAS-IRC. We have also continued several collaborations with Guy’s and St. Thomas’ Hospitals (radiotherapy, breast surgery, cardiology, dentistry). We have been awarded 5 TSB grants as part of their Technology Programme, have 4 grants awarded under the EU FP7 programme, and a significant portfolio of research income from EPSRC including a Platform Grant. Our income is running at about £4M pa.

Example Personal Achievements by CMIC members: Dean Barratt was awarded a Royal Academy of Engineering/EPSRC Research Fellowship in 2006. Professor Hawkes gave the Willhem Conrad Roentgen Honorary Lecture at the ECR in Vienna in 2006 and the Crookshank Lecture at the FRCR in May 2008. Daniel Alexander was appointed as Associate Editor for the IEEE TMI journal. Sebastien Ourselin was appointed Associate Editor for IEEE TMI and MedIA. He was also elected board member of Medical Image Computing and Computer Assisted Interventions (MICCAI), our most prestigious international conference series, in 2008. We were delighted and very proud that international conference prizes were awarded to the following PhD students: Tim Carter (MIAR in 2006), Irina Waechter (AAPM 2007 and MICCAI 2007). Steve Thompson (conference highlight at the European Association of Urologists annual meeting 2008).

Research Profile: The Centre’s activities are built around a matrix of applications and methodology. Methodological research includes image reconstruction and inverse problems, imaging moving structures, diffusion imaging and tractography, relating image derived information across scale, multi-modal imaging, image registration, data fusion, visualisation, measurement, shape representation and soft tissue motion correction. Our applications range from improving understanding of structural neuroanatomy and its changes during development, healthy ageing and disease, to expanding activity in image guidance technologies including interventional MR in both cardiovascular sciences and neurosciences, image guided neurosurgery, image guided ENT and maxillo-facial surgery, image guided orthopaedic surgery, and image directed focal therapy in the lung, liver and prostate. Learning, parametrising and correction of tissue motion, in particular due to respiration, has emerged as a major area of activity. In November last year we performed the world’s first deformable model based image guided breast surgery. In oncology (specifically in breast cancer) we are starting work on how imaging can be used to relate appearances and changes in appearance of specific phenotypes to discoveries of risk factors associated with specific cancer mutations. Digital tomosynthesis is emerging as an interesting technology for reconstructing breast macro-structure and its change over time.

We have a strong track record in presenting at peer reviewed international conferences. These include MICCAI, IPMI, ISMRM, ISBI, ICIP SPIE and MMBIA. We have a prolific research output in all the major international journals in our

field in particular in IEE Transactions on Medical Imaging, Medical Image Analysis, Neuroimage, Magnetic Resonance in Medicine, Inverse Problems, Medical Physics and Physics in Medicine and Biology. CMIC members are well represented on the editorial boards of these journals. We do need to increase our profile in the major clinical and biomedical journals.

Training: We have a strong PhD programme with 24 full time and 2 part time PhD students currently registered. Since 2005, 7 of our PhD students have successfully defended their PhD theses. In Sept 2007 we started running an MSc course on Medical Image Computing, sharing modules with the VIVE and Medical Physics MScs.

Relation to Biomedicine at UCL: At present CMIC sits within the Department of Medical Physics and Bio-Engineering in the Faculty of Engineering. In 2006 Professor Hawkes led the successful UCL bid for an MRC Interdisciplinary Bridging Award (IDBA) in biomedical imaging. we have used this relatively small amount of funding to help establish a network of researchers across the campusses and NIMR, who are interested in Biomedical Imaging. We are making progress in establishing this network on a more comprehensive and formal basis. CMIC will be working increasingly closely with the Centre for Advanced Biomedical Imaging (CABI), UCL’s preclinical imaging resource, to provide a translational pipeline for imaging at UCL. CMIC has also established strong links with the Medical Physics Group in the Institute of Nuclear Medicine (INM) headed by Professor Brian Hutton. This group’s expertise complements that available in CMIC with mutual interests in image reconstruction and general medical image processing but with a focus on applications in emission tomography (PET/CT and SPECT/CT). The INM physics group currently has a senior lecturer, three postocs and 4 PhD students with funding from various sources including commercial companies, research councils and EU FP6.

Imaging and Neuroimaging were two of the fourteen themes for the NIHR Comprehensive Biomedical Research Centre awarded to UCLH.At the time of writing, discussions are underway to coordinate imaging activity across the whole spectrum of UCL Biomedical Sciences and as a result CMIC is likely to develop much closer links with the Faculties of Biomedical Sciences and Life Sciences.

This booklet contains an entry for every researcher in CMIC. It starts with a summary of how each researcher links to both applications and methodology and then provides a short summary of each Principle Investigator’s research interests, followed by an alphabetical list of every CMIC researcher and their research topic. Further information of our activity can be found at www.ucl.ac.uk/cmic.

Professor David Hawkes FREng, FInstP, Director CMIC, May 2008

MethodologyImage RegistrationYu Bai, Tim Carter, Matt Clarkson, Gang Gao, John Hipwell, Yassir Jafar, Kelvin Leung, Tryphon Lambrou, Andrew Melbourne, Marc Modat, Jamie McClelland, Margarita Núñez, Yanni Papastavrou, Ged Ridgway, Christine Tanner, Zeike Taylor, Benjamin Thomas, Steve Thompson, Guang Yang.

Biomechanical ModellingHubert Fonteijn, Matt Hall, Yipeng Hu, Shahrum Nedjati-Gilani, Marc Modat, Eleftheria Panagiotaki, Christine Tanner,Zeike Taylor.

SegmentationMatt Clarkeson, Irving Dindoyal, Tryphon Lambrou, Kelvin Leung, Sarah McQuaid, Margarita Núñez, Tony Shepherd, Benjamin Thomas.

Shape ModellingCarolyn Chan, Irving Dindoyal, John Hipwell, Tony Shepherd, Steve Thompson, Athanasios Zacharopoulos.

Image ReconstructionJenny Edgar, Hubert Fonteijn, Matt Hall, K Kacperski, Shelan Mahmood, Shahrum Nedjati-Gilani, Margarita Núñez, Freddy Odille, Eleftheria Panagiotaki, Martin Schweiger, Kiran Seunarine, Vadim Soloviev, Christine Tanner, Irina Waechter, Mark White, Guang Yang, Athanasios Zacharopoulos.

Motion ModellingMingxing Hu, Yipeng Hu, Freddy Odille, Sarah McQuaid, Andrew Melbourne, Jamie McClelland, Irina Waechter, Mark White.

Intelligent Image AcquisitionJenny Edgar, Matt Hall, Shahrum Nedjati-Gilani, Freddy Odille, Eleftheria Panagiotaki.

Multiscale ImagingBaptiste Allain, Bailiang Chen, Peter Del-Manso, Hubert Fonteijn, Mingxing Hu, Shahrum Nedjati-Gilani, Eleftheria Panagiotaki, Kiran Seunarine.

VisualisationBaptiste Allain, Tim Carter, Matt Hall, Shahrum Nedjati-Gilani, Eleftheria Panagiotaki, Kiran Seunarine, Tony Shepherd.

Physics Based ModellingPhani Chinchapatnam, Gang Gao, Yuan Ruan.

ClassificationTryphon Lambrou, Yanni Papastavrou, Ged Ridgway, Benjamin Thomas.

Artefact CorrectionK Kacperski, Sarah McQuaid.

InstrumentationShelan Mahmood.

MeasurementJamie McClelland, Irina Waechter.

ApplicationsNeuroscienceYu Bai, Matt Clarkson, Matt Hall, , Hubert Fonteijn, Kelvin Leung, Marc Modat, Shahrum Nedjati-Gilani, Eleftheria Panagiotaki, Ged Ridgway, Martin Schweiger, Kiran Seunarine, Tony Shepherd, Vadim Soloviev, Zeike Taylor, Benjamin Thomas, Athanasios Zacharopoulos.

OncologyBaptiste Allain, Tim Carter, John Hipwell, Mingxing Hu, Yipeng Hu, Yassir Jafar, Tryphon Lambrou, Andrew Melbourne, Jamie McClelland, Yanni Papastavrou, Tony Shepherd, Martin Schweiger, Vadim Soloviev, Christine Tanner, Steve Thompson, Mark White, Guang Yang, Athanasios Zacharopoulos.

CardiovascularPhani Chinchapatnam, Irving Dindoyal, Jenny Edgar, Mingxing Hu, K Kacperski, Sarah McQuaid, Freddy Odille, Yuan Ruan, Irina Waechter

OrthopaedicsPeter Del Manso, Carolyn Chan, Bailing Chen.

Image Guided InterventionsBaptiste Allain, Carolyn Chan, Jenny Edgar, Hubert Fonteijn, Matt Hall, Mingxing Hu, Yipeng Hu, Kiran Seunarine, Steve Thompson.

Other Clinical AreasMargarita Núñez.

CMIC Principal Investigators

Danny AlexanderReader in Imaging Sciences

I work at the interface of medical imaging and computer vision. My recent work has been in neuroimaging with magnetic resonance imaging (MRI) and, in particular, using diffusion MRI to explore the microstructure and connectivity of the brain. Diffusion MRI measures the statistical displacement of water molecules in tissue over the millisecond range. The scattering of molecules over such times reflects the tissue microstructure.White matter is the cabling that links between different processing centres in the brain. White matter contains bundles of nerve fibres or axons; the figure shows a microscope image of a cross section through a bundle of nerve fibres in a white matter tract. Water molecules move further along the fibres than across, because their motion is less hindered by cell walls. We can infer the direction of greatest diffusion from diffusion MRI measurements, which provides an estimate of the fibre direction in each image voxel. Tractography algorithms follow those fibre orientation estimates from point to point to reconstruct the global connectivity.Much of my work has been on or related to tractography, but my main current interest is in inferring more subtle features of tissue microstructure from diffusion MRI. In particular, scattering depends on the density, size and permeability of cells, so diffusion MRI measurements support inferences about these microstructural features. Tissue microstructure is a window on tissue function and changes in microstructure are often early signs of disease. Measurements of specific microstructural features potentially provide early and more specific diagnostic information and invaluable biomarkers for development and treatment monitoring.I also work on various other quantitative MRI and imaging techniques with similar aims and continue to run various projects in computer vision, image and audio processing.

GrantsMonte-Carlo simulation framework for diffusion MRI 09/2007-08/2010. £380,904The optimisation of diffusion MRI to quantify fluid flow within the brain 01/2008-12/2009. £ 101,141 EPSRC CASE Studentship with GSK. 01/2008-12/2010EPSRC CASE Studentship with Biotronics3D 10/2005-09/2008.

PhD StudentsYu Bai, Model-Based Registration to Correct for Motion between Acquisitions in Diffusion MR Imaging Shahrum Nedjati-Gilani, Regularized Super-Resolution for Diffusion MRIKiran Seunarine, Exploiting the fibre-orientation distribution for probabilistic tractography Tony Shepherd, Supervised Methods for Perfect Segmentation in Medical Images Eleftheria Panagiotaki, 3D Model Construction from Real Brain-Tissue Images

Simon ArridgeProfessor of Image Processing

Research SummaryMy research is primarily in tomography for medical imaging, specifically the application of inverse problem techniques to image reconstruction. Inverse Problems can be linear or non-linear, and either well posed or ill-posed. Ill-posed inverse problems usually require regularisation techniques which can be placed within the general framework of Bayesian estimation, where the assumed prior distribution of the image under consideration plays the role of a penalty term in a constrained or unconstrained posterior probability optimisation.A topic of research for the last 20 years has been the development of optical tomography an imaging modality detecting the contrast of absorption and scattering of light in the visible and near-infrared region of the spectrum. In this wavelength range photons are so strongly scattered that they are quite well described by a diffusion or random-walk process in which the density of photons follows a Gaussian distribution with respect to distance from a source. The inverse problem for this imaging modality is non-linear and ill-posed. Significant improvements in image quality can be gained by using time-of-flight measurements using photon-counting detectors. This technique has been pioneered in the Medical Physics department where I did my PhD, and is one of the co-hosts of CMIC. Optical tomography extends to fluorescence optical tomography in which the contrast is the stimulated emission of light at another wavelength, discriminated from the background by spectral filtering, and photoacoustic tomography where the contrast is stimulated emission of ultrasound waves, which do not suffer significantly from attenuation.Other research topics that use similar methods are fast cardiac MRI, digital tomosynthesis, electro cardio physiology, and nuclear medicine (SPECT and PET). As well as the use of generic priors such as distribution of edges, I am currently investigating the use of cross-modality priors which involves other topics such as information-theoretic measures, probabilistic atlases, and image registration.

PhD StudentsChristos Panagiotou - Combined Reconstruction and Registration Using Information-Theoretic Priors Yuan Ruan - Inverse Electrophysiology of the Heart Guang Yang - Digital Breast Tomosynthesis

GrantsMolecular Imaging , CEC Framework VI, £224,596, 1/04-12/08Development of Novel Optical And Photoacoustic Instruments for Clinical Diagnosis and Monitoring, EP/C500210/1 (platform grant), £423,328, 10/05-9/10Optical Tomography of the Neonatal Brain, EPSRC EP/D502330/1, £326,084, 11/05-11/07Multispectral Quantitative Image Reconstruction Methods for Photoacoustic Molecular Imaging, EPSRC EP/D069181/1, £327,327, 3/06-3/09.The UCL Centre for Medical Image Computing, EPSRC EP/D506468/1 (platform grant), £ 418,297, 8/06-7/11.Electro-anatomical Fusion for Guiding EP Procedures and Patient Specific Modelling, EPSRC EP/D060877/1, £ 295,025, 10/06-9/09.Parameter and Structure Indentification in Optical Tomography, EPSRC EP/E034950/1, £ 629,463, 11/07-10/10.Digital Breast Tomosynthesis, DTI/EPSRC DT/F002785/1, £296,743, 2/08-1/11.Neuropt, CEC Framework VII, Euro 479,906, 4/08-3/12.FMT-XCT, CEC Framework VII, Euro 541,600, 6/08-5/12.

CollaboratorsJ. Kaipio, V. Kolehmainen, T. Tarvainen, University of Kuopio.J. Ripoll, FORTH, Crete.A.Yodh, U.Penn.D. Boas, Massachusetts General Hospital, Boston.O.Dorn, University Carlos III, Madrid.C d.Andrea, G.Valenti, R.Cubeddu, Politechnico di Milano.H. Rinneberg, H. Wabnitz, R. MacDonald, Physikalisch-Technische Bundesanstalt Berlin.

David AtkinsonLecturer

David Atkinson obtained a PhD from UCL in 1991 on the design and characterisation of semiconductor optical modulators. He then worked as a scientific programmer for Tessella Support Services simulating oil extraction. In 1992 he returned to UCL as a PostDoc to work on short-pulse fibre lasers, semiconductor saturable absorbers and the modelling of optical fibre telecommunications systems. In 1996 he joined the group of Derek Hill and Dave Hawkes in the medical school at Guy’s Hospital to use his signal processing expertise in the correction of Magnetic Resonance Images for the effects of patient motion. In 2000, he spent 5 months in the Engineering Science group at Oxford University researching 3D cardiac ultrasound imaging before starting a 5 year EPSRC Advanced Research Fellowship at Guy’s. In 2005 he joined the newly formed Centre for Medical Image Computing at UCL where he is now a Lecturer.Current research interests include a range of techniques and applications for avoiding the unwanted effects of motion and flow in MR imaging. For motion occurring on a timescale faster than the acquisition of a single image, he is involved in projects to either determine the motion from training data or infer it from physical sensors, or use complementary information from multiple receiver coils. When there is motion between scans, such as in repeat breath-holds for liver imaging, he is involved in developing registration algorithms to align images, even in the presence of contrast changes due to the injection of a contrast agent. The use of graphics cards to speed-up image registration, enable new biomechanical models, and make the reconstruction of cardiac images faster than the time required to acquire the undersampled data are also active areas of research. For many years, he has also had an interest in high-resolution diffusion imaging of the brain and the figure shows a recent image acquired as part of this ongoing research.

GrantsHPC Software for Medical Imaging EPSRC £173,695 10/2007 - 03/2009Time-resolved whole-heart cardiac imaging using highly parallel magnetic resonance EPSRC £357,681 05/2007 – 04/2010PhD StudentsJenny Edgar (1st year) – Fast cardiac imaging. Andrew Melbourne (3rd year) – Registration of images with varying contrast.PostDoctoral ResearchersFreddy Odille - Time-resolved whole-heart cardiac imaging using highly parallel magnetic resonance.Zeike Taylor - HPC Software for Medical Imaging

CollaboratorsAarhus University Thomas Sangild SorensenImperial College London Serena Counsell, Jo Hajnal, David Larkman, Rita Nunes.Kings College London Philipp Batchelor, Claudia Prieto, Reza Razavi, Tobias Schaeffter, Sergio Uribe, Mohammed Usman.Royal Marsden Hospital, Sutton. David Collins, Dow-Mu Koh, Martin Leach, Keiko Miyazaki, Matthew Orton. Siemens Medical Systems David Porter.

Figure showing coronal slice and zoomed region of the hippocampus from a volunteer imaged with a multi-shot diffusion tensor sequence. From left to right, an image weighted by the diffusion tensor trace, the mean diffusivity, the fractional anisotropy (FA) and the FA colour coded by fibre direction. The yellow arrow highlights hippocampal structure not usually observed on conventional diffusion weighted imaging. Data acquired at Institute of Child Health on a 1.5T Siemens Avanto.

Dr Dean BarrattRoyal Academy of Engineering/EPSRC Research FellowHonorary Lecturer and Senior Research Fellow

Research SummaryMy primary research interest is in 3D ultrasound imaging, in particular, its role in guiding needle biopsy and surgical interventions. Much of my current research focuses on aspects of the problem of registering one or more diagnostic quality MR, CT, or nuclear medicine images to ultrasound (US) images obtained during a surgical procedure. The ability to perform this registration accurately, automatically, and rapidly is critical for enabling more effective image guidance during procedures where US provides a low-cost and ubiquitous method for localising anatomical structures, but typically only provides limited information due to a restricted field-of-view, poor contrast between different types of soft tissue, or the difficulty associated with interpreting US images.I am currently investigating a number of approaches to solving this registration problem for a range of clinical applications as part of a 5 year Royal Academy of Engineering/EPSRC Research Fellowship. Techniques that I am particularly interested in include combined statistical-biomechanical modelling of organ motion, vessel-based registration, self calibration (of freehand 3D US systems), and imaging-model-based registration that utilises US wave propagation modelling methods to generate synthetic US images from MR and CT images. Recent clinical applications include neurosurgery (brain tumour resection), orthopaedic surgery (total hip replacement), high-intensity focused US (HIFU) ablation of liver metastases, and biopsy and minimally-invasive therapies for the diagnosis, mapping, and treatment of prostate cancer.

PhD StudentsYipeng Hu (1st year) – Modelling prostate gland motion for guiding biopsy and minimally-invasive interventions for the treatment of prostate cancerIrina Waechter (3rd year) – Using flow information to support 3D vessel reconstruction from rotational angiography images

GrantsUltrasound Image Registration for Guiding Medical Interventions (PI), Royal Academy of Engineering/EPSRC Research Fellowship £537,752 10/2006 – 09/2011Transcostal High Intensity Focused Ultrasound for the Treatment of Cancer (CI), EPSRC Grant EP/F025750/1: £1,541,603 03/2008 – 02/2013.Image-guided Prostate Device (CI), UCL Business Plc. Proof of Concept Grant (Poc-08-005) £24,024 04/2008 – 12/2008A series of engineering events at the BA Festival of Science 2008 (CI), Royal Academy of Engineering Ingenious Award £29,090; 9 months

CollaboratorsMr. Mark Emberton and Mr. Hashim Ahmed, Department of Urology, UCLHDr. Clare Allen, Dr. Alex Kirkham and Prof. Steve Halligan, Department of Radiology, UCLHProf. Steve Bown, National Medical Laser Centre, UCLDr. Nader Saffari and Dr. Eleanor Stride, Department of Mechanical Engineering, UCLProf. Gail ter Haar and Dr. Ian Rivens, Institute of Cancer Research, SuttonDr. Constantin Coussios, Department of Engineering Science, University of OxfordDr. Steve Pereira, Institute of Hepatology, UCLMr. Neil Kitchen and Prof. David Thomas, National Hospital for Neurology and Neurosurgery

Professor David Hawkes, FREng, FInstPDirector of the Centre for Medical Image Computing

Dave Hawkes is currently the Director of the Centre for Medical Image Computing, previously having been Director of the EPSRC and MRC funded Interdisciplinary Research Collaboration on Medical Images and Signals (MIAS-IRC), an £8M six year programme, from 2003 to 2007 and Chairman of the Division of Imaging Sciences at KCL (2002-2004). He spent 10 years working as a clinical scientist within the NHS before returning to academia. He is co-Founder of IXICO Ltd. (www.ixico.com), a university spin-out that provides imaging solutions to the pharmaceutical industry. His current research interests encompass image matching, data fusion, visualisation, shape representation, surface geometry and modelling tissue deformation promoting medical imaging as an accurate measurement tool and image guided interventions. He is principal investigator of three EPSRC funded projects (in breast cancer diagnosis, image guidance in totally endoscopic cardiac interventions, analysis of perfusion in liver metastases) and co-PI on 4 other EPSRC Grants (in high intensity focused ultrasound, cardiac MRI segmentation, image guided cardiac ablation, high performance computing), and an NIHR grant with Steve Halligan (Radiology) on computer assisted detection of colon cancer. He leads an EPSRC Platform Grant and is manager of three industrially sponsored projects (Carestream on X-ray mammography analysis, VisionRT on tracking motion in lung radiotherapy, Dexela on X-ray digital tomosynthesis). He is currently supervisor or co-supervisor of 10 PhD students.

GrantsTSB (previously DTI): Digital X-ray Tomosynthesis, with Dexela, £283k, 2008-2011EU FP7: PASSPORT (liver surgery simulation), 320,000 EurosEU FP7: HAMAM (multimodal breast cancer imaging workstation), 540,573 EurosEPSRC: Model Based Analysis of X-ray Mammograms, with Oxford, Manchester, UCL award £385k, 2007-2010EPSRC: Model-based 2D-3D registration and tracking of deformable objects for image-guided minimally invasive cardiac interventions, £229k, 2006-2009 MRC: IDBA Bridging Disciplines in Multiscale, Multidimensional and Time Series Imaging £326k, 2006-2009 EPSRC: Evaluating Vascular Properties of Tumours in the Presence of Tissue Motion: Application to Functional Studies of Liver Tumours £279,218, 2005-8EPSRC: The Centre for Medical Image Computing (Platform Grant), £418k, 2006-2011CRUK: Correction for motion in lung RT £300k, 2004-2007DoH HTD: Video based compensation for motion in lung RT £300k, 2005-2008

EPSRC: Model-based 2D-3D registration and tracking of deformable objects for image-guided minimally invasive cardiac interventions £228,992, 2006-9 (PI Derek Hill)EPSRC: Minimal Access Navigated Orthopaedic Surgery (MAcNavOS), £398,286, 2005-2008DTI: Technology Programme: Improved Breast Cancer Detection £300k, 2005-2008DTI: MAcNavOS, ~£300k, 2005-2008

PhD StudentsBaptiste AllainNathan CahillTim CarterYipeng HuYassir JafarMarc ModatSteve ThopmsonIrina WaechterXiahai Zhuang

Collaborators:UCL:Prof Steve Halligan, Radiology, UCH.; Dr Mark Lythgoe, CABI; Mr Mark Emberton, Urology, UCH; Prof Steve Bown, National Laser Centre, UCH; Dr. Nader Saffiri, Mechanical Engineering, UCL; Prof Mike Horton, London Centre for Nanotechnology; Professor Tarek Yousry, Neuroradiology, IoN; Professor Nick Fox, Neurology, IoN

External:Professors Martin Leach, MR Physics; Steve Webb, Radiotherapy Physics; Mike Brada, Radiation Oncology; Gail ter-Haar, Therapeutic Ultrasound, Institute of Cancer ResearchProfessor Reza Razavi, Cardiologist; Dr. David Landau, Radiation Oncologist; Dr. Philipp Batchelor, Mathematician; Dr. Graeme Penney, Imaging Scientist; Mr.Prokar Dasgupta, Urologist; Mr Richard Cook, Dental Surgeon, KCL and Guy’s and St Thomas’ Trust.Professor Sir Michael Brady, Imaging Scientist; Professor Alison Noble, Imaging Scientist;, Dr. Constantin Coussios, Biomedical Engineer, Oxford University.Professor Chris Taylor, Imaging Scientist; Dr Sue Astley, Imaging Scientist. University of ManchesterDr. Kensatu Mori, Imaging Scientist, Nagoya University; Professor Kevin Cleary, Georgetown, Washington; Professor Terry Peters, London, Ontario.Professor Heinz-Otto Pietgen, Imaging Scientist, University of Bremen; Professor Luc Soler, University of Strasbourg; Professor Herve Delingette, INRIA, Nice; Professor Nassir Navab, TU Munich; Professor Gabor Sezekely, ETH Zurich; Nathan Cahill, Carestream; Ed Bullard, Dexela; Derek Hill, IXICO; Norman Smith, VisionRT; Juergen Weese, Philips Research Aachen

Derek HillProfessor of Medical Imaging Science

Derek Hill is co-founder and Chief Executive Officer of IXICO. He founded IXICO to bring the best possible imaging technology and know-how to the pharmaceutical and medical device industries. Derek also holds an academic appointment as Full Professor at University College London. He took a BSc degree in Physics at Imperial College, an MSc in Medical Physics at University of Surrey, a PhD in medical image analysis at the Medical School of Guy’s & St Thomas’ Hospitals, University of London, and business training at the London Business School through the CSEL programme. He was appointed a lecturer (assistant professor) in 1995. He was appointed full professor in September 2004, shortly before co-founding IXICO. He has been working on medical image analysis for nearly 20 years, and has authored more than 60 journal papers in this field. His research track record includes image acquisition and analysis for drug discovery and development, other aspects of image acquisition including motion correction and partially parallel imaging in MRI and motion compensation in PET. He has worked on applications in the study of dementia, heart disease, arthritis, oncology and guiding interventions. Much of this work was in collaboration with the medical imaging, medical device and pharmaceutical industries. He is a member of the MRI Core of the ADNI project, is an associate editor of IEEE Transactions on Medical Imaging, and is a member of the ISMRM safety committee and the ISMRM Ad Hoc Committee on Standards for Quantitative MR.

GrantsElectro-anatomical Fusion for Guiding EP Procedures and Patient Specific Modelling EPSRC £295,025 10/2006 – 09/009Acquisition and analysis of dynamic 3D cardiac volumes with MRI EPSC £391,8061NeuroGRID MRC £338087 01/2005 - 08/2008

PhD StudentsGed RidgwayXiahai Xhuang

Derek is currently seconded to IXICO Ltd.

Professor Brian F HuttonChair of Medical Physics in Nuclear Medicine and Molecular Imaging ScienceInstitute of Nuclear Medicine, UCL and UCLH NHS Foundation Trust

Research SummaryI head a Medical Physics research group in the Institute of Nuclear Medicine, a combined academic and service department, well equipped with state of the art imaging equipment including PET/CT (2) and SPECT/CT (2). The research physics group was established in 2004 and now has a wide spectrum of research on Nuclear Medicine physics topics. My personal research interests cover a wide range of topics, particularly related to quantitative aspects of emission tomography (both SPECT and PET) and more recently multi-modality imaging. This extends from system design and image reconstruction to corrections for attenuation, scatter, resolution and motion and multi-modal image registration as well as quantification (e.g. tracer kinetics) and application to clinical research.

The group currently focuses on projects in a number of areas:Development and evaluation of novel SPECT instrumentation: High intrinsic resolution system (HICAM), D-SPECT (first European system)Optimised image reconstruction: motion correction for low-dose CT, resolution modellingImage processing for multi-modal studies (PET/CT and SPECT/CT): PET/CT motion correction, head and neck registration, classification in dementia studies, partial volume correctionQuantification and tracer kinetic modelling: CT perfusion and Rubidium-82 perfusion

PhD StudentsYanni Papastavrou (3rd year) – Non-rigid registration for head and neck PET-CT (INM)Sarah McQuaid (2nd year) – Motion correction for cardiac PET-CT (BBSRC+GSK)Shelan Mahmood (2nd year) – Design of a novel brain SPECT system (EPSRC+GE)Benjamin Thomas (1st year) – Classification in dementia based on multi-modality data (EPSRC+GE)Margarita Nunez (2nd year) – Quantitative lung SPECT (Univ Republic Uruguay)

GrantsHICAM: Development of a high resolution Anger camera EC FP6 1.7M euros; UCL component: 387,000 euros; 2007-2010.GE Healthcare: Optimising CT for use with SPECT/CT £75k 2006-2009Tumour Angiogenesis in Breast Cancer; Breast Cancer Campaign: £170k 2006-2008

CollaboratorsDr Kjell Erlandsson, Dr Krzysztof Kacperski, Dr Tryphon Lambrou, INM, UCLDr John Dickson, Dr Matthew Aldridge, Ms Wendy Waddington, INM UCHProfessor Peter Ell, Dr Ashley Groves, Professor Simona Ben-Haim, INM, UCHProfessor David Hawkes, Professor Simon Arridge, Dr Sebastien Ourselin, CMIC, UCLDr Irene Buvat, INSERM, Paris, FranceProfessor Freek Beekman, Univs Delft and Utrecht, NetherlandsProfessor Carlo Fiorini, Polytechnic, Milan. ItalyProfessor Michael King, Dr Joaney Dey, Dr Hendrik Pretorius, UMAss Worcester, USAProfessor Anatoly Rosenfeld, Univ Wollongong, NSW, AustraliaProfessor Thanasis Fokas, Dept Mathematics, Cambridge

Industrial collaborators / sponsors:GE Healthcare, Spectrum Dynamics, Nuclear Fields, GSK

Sebastien Ourselin, PhDReader in Medical Image Computing

Sebastien Ourselin obtained his PhD in Computer Science from INRIA (Sophia-Antipolis, France) in the field of medical image analysis. He is currently Reader in Medical Imaging Science within the Centre for Medical Image Computing (CMIC) at the University College London, United Kingdom. He has published more than 80 journal and conference articles. He is an associate editor of Medical Image Analysis and a MICCAI Board Member. His main research interests include rigid and non-rigid registration, molecular imaging, segmentation, atlas conception, statistical shape modeling, surgical simulation, image-guided therapy and minimally invasive surgery. Sebastien Ourselin has been involved in a broad range of clinical projects, including neuroimaging, neurosurgery, computer-assisted cardiovascular surgery, radiotherapy treatment, colonoscopy, and orthopaedic research. His research is often collaborative and involves universities, clinical and commercial partners.In collaboration with Professor Nick Fox, he is currently building a joint research programme with the Dementia Research Centre (DRC) at the Institute of Neurology. The aim of this collaboration is to increase translation of CMIC methodological work into the clinic, with a strong focus on dementia. The research programme is also involving IXICO Ltd, a university spin-out that provides imaging solutions to the pharmaceutical industry.Sebastien is also leading the development of a dedicated medical image analysis platform for widespread use within the university and beyond. This platform will provide a plugin architecture to provide easy extension points, and in time will become the de facto working environment for all imaging research and trials across CMIC and DRC. The platform will bring together the expertise from many years experience in image registration, segmentation, image quantification and visualisation, leading to increased cross site collaboration and higher throughput.In his previous most recent role, from 2003 to November 2007, he founded and led the CSIRO BioMedIA Lab, Australia. In Australia, he is currently adjunct A/Prof at the University of Queensland (joint appointment Faculty of Health Sciences and School of Information Technology and Electrical Engineering), adjunct A/Prof at the University of New South Wales, Graduate School of Biomedical Engineering, and adjunct Senior Lecturer at the University of Sydney, department of Electrical Engineering. He is also Honorary Medical Physicist at Westmead Hospital (Sydney, Australia), Department of Radiology. Until December 2007, he was President of the Australian Pattern Recognition Society (http://www.aprs.org.au).He has been actively involved in conference organization, nationally and internationally. He serves on the SPIE International Technical Committee for Medical Imaging. He is part of the MICCAI 2008 Program Committee, and was Program co-chair of MICCAI 2007 (http://www.miccai2007.org). He is also part of the Steering Committee of ISBMS 2008, and serves as Technical Liaison for ISBI 2008.

GrantsTechnology Strategy Board (previously DTI): Imaging to assess efficacy of new treatments for Alzheimer’s Disease, £417K, 2008-2011 (PI Nick Fox)EPSRC Platform Grant: The Centre for Medical Image Computing, £418K, 2006-2011

PhD StudentsMarc ModatGed RidgwayBaptiste Allain

Andrew Todd-PokrpekProfessor of Medical PhysicsHead of Medical Physics and Bioengineering

My research interests range from tomographic reconstruction and correction of sources of error in for example SPECT and PET, to segmentation using methods including Active Shape Models, Level Sets, ICA based models, fuzzy connectivity, to classification techniques including ANNs, texture analysis using SVMs, to Computer Assisted Detection and Diagnosis. A new topic of research is that of multi-scale imaging where models are built and insubstantiated at various levels of detail. This project is aimed in particular at bone and cartilage and is funded in part by an EU Network of Excellence ‘3D Anatomical Human’. Other projects include liver segmentation and multi-phase liver arterial and venous segmentation to assist ablation therapy, analysis and quantification of vocal fold images, non-rigid registration techniques and evaluation in particular related to PET/CT, correction of Partial Volume effects, segmentation and quantification of spinal cord intervertebral disks, analysis of MRI images of muscles in functional electrical stimulation, quantification of attenuation correction in SPECT/PET. I also participate in various standards bodies including NEMA/ DICOM. Shown below are images of vertebral disk segmentation and reconstruction resolution improvement.

Ron GastonResearch Manager

Ron Gaston has been with CMIC since 1995. He joined the group as an undergrad student and carried out his honours year working on a project looking at geometric distortions in MR imaging. After graduating he joined CMIC (previously called the Computational Imaging Sciences Group) in 1997 as a researcher working on an image guided surgery project using an A mode ultrasound system called the Acoustic to collect bone points from the skull surface to update registrations during surgery. He took on more administrative responsibilities and became Divisional Manager at KCL and then Research Manager when the group moved to UCL in January 2005. Ron now manages the group and acts as the primary contact point between the group and UCL’s central services.

Model of Cube Phantom used to detect geometric distortion in magnetic resonance imaging.

CMIC Researchers(in alphabetical order)

Development of navigation and instrument location technologies for in situ confocal micro-endoscopy

PhD Student: Baptiste AllainSupervisors: David Hawkes (UCL), Richard Cook (KCL), Sebastien Ourselin (UCL)Academic Collaborators: Mark Lythgoe (UCL CABI), Anthony Price (UCL CABI), Fred Festy (KCL)Industrial Collaborator: Mauna Key TechnologiesFunding: DoH Clinician Scientist Award to Dr Cook - King’s College London Dental Institute, Biophotonics and Oral Medicine Groups.

Fibered confocal microscopy consists of acquiring real-time images of tissues at a biological level with a micro-endoscope. As mosaics of the acquired images can be reconstructed for a wide field of view, this technique is useful for in situ biopsies. Nevertheless, there is a lack of information about the micro-endoscope location in the inspected region. Therefore, the project consists of developing a navigation and location system for the micro-endoscope to analyse abnormalities for applications in gastroenterology.

As the micro-endoscope is only powerful in the analysed region, a macroscopic image is useful for the detection of abnormalities and for navigation. Therefore, a 3D imaging modality would help track the micro-endoscope and display the mosaic on a 3D reconstruction of the analysed region. Thus, a first step of the project consists of tracking the micro-endoscope with an interventional Magnetic Resonance (MR) scanner.As the diameter of the micro-endoscope is 300 μm or 650 μm, a first experiment intended to check the MR scanner resolution. A 650 μm-diameter micro-endoscope was inserted in a jelly phantom, then in a tomato, and scanned with a 9.4 T MRI scanner (Varian). The experiment showed that the scanner resolution is good enough to see the micro-endoscope, but that a marker at the micro-endoscope tip would be useful to distinguish it from air-filled regions.The next steps of the project will focus on the selection of an interesting marker, on tracking techniques with other 3D imaging modalities, and on the display of a mosaic on a 3D shape.

Use a 3D imaging modality for the micro-endoscope tracking

Display the mosaic on the 3D reconstruction of the analysed region

Use images from the micro-endoscope

Model-Based Registration to Correct for Motion between Acquisitions in Diffusion MR Imaging

PhD Student: Yu Bai. Supervisors: Daniel AlexanderFunding: EPSRC

In diffusion MRI, a number of diffusion-weighted (DW) images with different diffusion-weighting gradient directions are acquired during scanning. The diffusion tensor (DT) and other diffusion model calculations assume that each voxel corresponds to the same anatomical location in all the measurements. The long scan time introduces patient movement. Moreover, EPI induces displacement and distortion in DW-MRI. The traditional method uses a non-diffusion-weighted image as the reference for registration, but the differences between diffusion-weighted images and the non-diffusion weighted reference image can cause mismatching to occur during registration, even using metrics like mutual information (MI) that accounts for non-linear contrast differences. We propose alternative model-based methods to improve motion correction and avoid the errors that the traditional method introduces. The new methods are based on a three-step procedure to register DWI data sets. They use different reference images for DWIs with different gradient directions for registration, so the registrations take into account the contrast differences of the measurements. After aligning the data set, we also update diffusion gradients for the registered datasets from both traditional and our methods, according to the transformations used in registrations. Quantitative results show our model-based methods provide improvement from the traditional alignment procedure, and orientation correction for the diffusion gradients upgrades the registration performance for all methods.[1] Y. Bai and D. C. Alexander, “Model-Based Registration to Correct for Motion between Acquisitions in Diffusion MR

Imaging”, The Fifth IEEE International Symposium on Biomedical Imaging (ISBI 2008), May 2008. [2] Y. Bai and D. C. Alexander, “Correcting for Motion between Acquisitions in Diffusion MR Imaging”, Medical Image

Understanding and Analysis (MIUA2006), May 2006. [3] Y. Bai, P. A. Cook, H. Zhang and D. C. Alexander, “Motion Correction in Diffusion Magnetic Resonance”,

International Society for Magnetic Resonance in Medicine (ISMRM) 14th Scientific Meeting and Exhibition, May 2006.

Image-Guided Breast Surgery Research Associate: Tim Carter Principal Investigator: David Hawkes Funding: UCL Business Proof of Concept funding and EPSRC The most common treatment for breast cancer aims to excise the cancer and a small volume of surrounding healthy tissue, but to conserve the majority of the breast tissue. A disadvantage of this procedure is that a large proportion of these operations need to be repeated because, on histological examination, cancer is found at the margins of the surgical specimen indicating that it has not been completely excised. Targeting the operation using the spatial information contained in preoperative 3D dynamic contrast enhanced magnetic resonance (MR) images might help to reduce the high re-excision rate associated with breast conserving surgery. However, because the enhancing images must be acquired with the patient positioned prone but surgery is performed supine, a large deformation of the breast occurs that currently limits the usefulness of these images.

We have been investigating the ability of a biomechanical model to compensate for these large deformations. Prior to surgery we need to register MR images acquired without enhancement in the prone and supine positions, in order that a cancer indicated in a sequence of co-registered prone contrast enhanced images can be projected into the supine image. An initial estimate of the deformation field can be obtained by using a biomechanical simulation of the breast in order to overcome the difficulties caused by poor initial overlap between prone and supine MR images. The correspondence can then be improved using an intensity-based registration scheme. The same biomechanical model can be used to register the supine MR image with surgery by deforming the model to match the surface of the breast as reconstructed from stereo camera images. Our first clinical experience with this image-guidance system

was in November 2007. The system’s accuracy was assessed against tracked ultrasound images, and was determined to be around 5mm for this initial case.

Minimal Access Navigated Orthopaedic Surgery(MacNavOS) Research Associate: Carolyn ChanPrincipal Investigator: David Hawkes(UCL) Co-Investigator: Graeme Penney(KCL)Academic Collaborator: Dean Barratt (UCL)Funding: EPSRC This project brings together recent advances in image registration and shape modelling in an innovation that could revolutionise a very common orthopaedic procedure: total hip replacement (THR). This should bring major improvements to the accuracy and invasiveness of the procedure.We have previously introduced a novel approach using tracked ultrasound as a non-invasive localiser to simultaneously instantiate and register three-dimensional statistical shape models (SSMs) for image-guided hip replacement surgery, this uses bone surface points derived from a set of tracked B-mode ultrasound images acquired on humans, to instantiate a patient-specific 3D model of the relevant anatomy.The aims of this project are to carry out a detailed investigation into the construction of SSMs that are well powered statistically, to fully exploit the potential of these models and to carefully analyse how many datasets are required to specify the model with sufficient accuracy for our application; also to devise new methods for instantiating these models to guide a particular patient’s operation. Current progress into this project includes investigating how the accuracy of the model depends on the size of the dataset, and whether it is possible to predict the size of training set required for a particular model accuracy using only a small sample of data. Investigation is also under way in testing the model using a number of criteria: generalisation, compactness and specificity. So far the model produced using the old template, which was randomly chosen, when compared with the model produced using the new “averaged” template, gave slightly different average shapes, with the first five modes of variation looking similar.This proposed method has the potential to reduce the invasiveness of THR, and remove the requirement for a preoperative CT scan with its associated radiation dose, inconvenience and expense.

[1] Dean C. Barratt, Carolyn S.K. Chan, Philip J. Edwards, Graeme P. Penney, Mike Slomczykowski, Timothy J. Carter and David J. Hawkes. Instantiation and registration of statistical shape models of the femur and pelvis using 3D ultrasound imaging, Medical Image Analysis, available online 29 January 2008.

Multiscale Modeling and Imaging: The Challenges of Osteogenesis Imperfecta

PhD Student: Bailiang ChenSupervisors: Martin Fry(UCL) and Andrew Todd-Pokropek(UCL)Academic Collaborators: Tryphon Lambrou(UCL) Funding: EU 3D Anatomical Human

Osteogenesis Imperfecta (OI) is a genetic disorder characterized by bones that break easily, often with little or no apparent cause. A classification system of different types of OI is commonly used to help describe how severely a person with OI is affected. Clinical features of OI vary greatly from case to case. Currently, 8 known types of OI features have been reported. The majority of OI cases are caused by a dominant mutation in a gene coding for type I collagen (namely type I, II, III and IV OI), while types V and VI have no type I collagen mutation. Newly discovered types VII and VIII are inherited in a recessive manner. Unfortunately, no cure has been found for OI [1]. Such dramatic clinical presentations have put OI on the leading edge of discovery of the methodologies and principles that apply for other inheritable diseases of connective tissues. Quantification and interpretation are clearly needed in order to provide a better understanding of OI, which may possibly lead to an improved prognosis of OI. Imaging to provide clinical diagnosis of OI is normally based on x-rays, with other modalities used as problem-solving methods, e.g. MRI for neurological symptom [2]. However, such imaging techniques are practically limited to the resolution of ~1mm. The special nature of OI requires much more detailed information associated with tissues and cells to visualize the structural changes. Such information has the potential to help the quantification of severity of OI status, leading to a more suitable care for patients. Thus finer level information will assist the clinically-achieved coarser level images to provide better quantitative diagnosis of OI types, and enable more appropriate care via a model-based imaging procedure. For this reason a multi-scale modelling and imaging method is being developed.In general, multi-scale methods aim to relate information from different scales (molecular, genetic, cellular, tissue and organ) by scale bridging to achieve a better understanding of complex systems such as human organs. In the hierarchical self-organization of organs, the macroscopic appearances are the accumulations of the underlying layers, while the microscopic structures are significantly affected by the macroscopic level interactions. OI is indeed a good example with great demands and potential to benefit from this approach as the distinct structural changes at the cellular level have been demonstrated using second harmonic generation (SHG) imaging microscopy [3]. In this multi-scale method, data will be acquired from cellular level to macro level through different imaging protocols (T1, T2, and DTI/DWI MRI) and resources (ultrasound, and SHG imaging microscopy). Based on these data, a fine model representing microscopic structural properties of bone will be built to combine the acquired data via modelling techniques including finite element methods (FEM). However, multi-scale modelling and imaging is difficult as it explores the bridging across scales, and seeks mathematical models to define morphological and physiological differences, and also requires progressive improvements of the imaging and modelling techniques. Computation complexity, data acquisition, and validation are also challenges in multi-scale methodology.

References[1] Osteogenesis Imperfecta Overview”, National Institute of Health Osteoporosis and Related Bone Disease Center

http://www.niams.nih.gov/Health_Info/Bone/Osteogenesis_Imperfecta/default.asp [2] A. Kirpalani, P.S. Babyn, “Osteogenesis Imperfecta”, EMedicine, December 2005, http://www.emedicine.com/

Radio/topic497.htm [3] O. Nadiarnykh, S. Plotnikov, W.A. Mohler, I. Kalajzic, D. Redford-Badwal, P.J. Campagnola, “Second

harmonicgeneration imaging microscopy studies of osteogenesis imperfecta”, Journal of Biomedical Optics, 12, 5, pp. 051805, 2007.

Electro-anatomical fusion for guiding EP procedures and patient specific modelling

Research Associate: Phani Chinchapatnam Principal Investigator: Derek Hill (UCL) Co-Investigator: Simon Arridge (UCL)Academic Collaborators: Reza Razavi (KCL) Maxime Sermesant (INRIA/KCL)Funding: EPSRC

Cardiac arrhythmia is a cause of considerable morbidity and mortality in addition to constituting a huge cost burden to modern health-care systems. This project focuses on development of biophysical models and integration of electro-anatomical data to image electrical parameters of the heart. Development of such functional imaging reveal that hidden parameters of the heart can be instrumental for improved diagnosis and planning of therapy for cardiac arrhythmia and heart failure, for example during procedures such as radio-frequency ablation and cardiac resynchronisation therapy. Existing biophysical models however are computationally expensive and are presently not suitable for direct use in the cardiac catherisation laboratory. An eikonal model for electrical propagation on the cardiac tissue satisfying clinical constraints of time has been developed as part of this project [1]. The developed model has been employed to estimate hidden conductivity parameters of the heart and also to predict electrical propagation for different pacing sites. Validation has been performed on clinical data obtained during electrophysiology study [2].

[1] M. Sermesant, E. Konukoglu, H. Delingette, Y. Coudiere, P. Chinchapatnam, K. S. Rhode, R. Razavi and N. Ayache, “An anisotropic multi-front fast marching method for real-time simulation of cardiac electrophysiology,” in Functional Imaging and Modelling of the Heart, 2007

[2] P. Chinchapatnam, K. S. Rhode, A. P. King, G. Gao, Y. Ma, T. Schaeffter, D. J. Hawkes, R. Razavi, D. L. G. Hill, S. R. Arridge, and M. Sermesant, “Anisotropic wave propagation and apparent conductivity estimation in a fast electrophysiological model: Application to XMR interventional imaging,” in Medical Image Computing and Computer Assisted Intervention (1), vol. 4791. Springer, 2007, pp 575-583

Estimated conduction parameters using eikonal model and validation against scar locations for a patient case with left bundle branch block pathology

Use of imaging to assess efficacy and safety of new treatments for Alzheimer’s Diseases (AD)

Research Associate: Matt ClarksonPrincipal Investigator: Nick Fox (UCL) Co-Investigator: (UCL) Sebastien OurselinAcademic Collaborators: Jo Barnes (UCL), Marc Modat (UCL), Kelvin Leung (UCL)Industrial Collaborator: Ixico LtdFunding: Technology Strategy Board

The aims of the project are to develop novel imaging biomarkers that will make quantitative measurements of the effect of treatments that complement methods such as cognitive testing and also to develop serial MR imaging techniques to detect therapy induced inflammation and adverse vascular effects. This work will include automation of image measurements such as the Boundary Shift Integral, the calculation of hippocampal volume and cortical thickness, the use of non-rigid registration to assess longitudinal changes in grey matter, white matter and ventricles and the development of statistical methods to assess spatial and longitudinal changes in groups of subjects. For longitudinal studies with two or more time points, a spatio-temporal model will be developed to further enhance the sensitivity and robustness of these algorithms.Additionally, Matt is working on a dedicated medical image analysis platform for widespread use within the university and beyond. This platform will provide a plugin architecture to provide easy extension points, and in time will become the de facto working environment for all imaging research and trials across CMIC and DRC. The platform will bring together the expertise from many years experience in image registration, segmentation, image quantification and visualisation, leading to increased cross site collaboration and higher throughput.

Using Freesurfer to measure change in cortical thickness in a group of patients with semantic dementia

Multi-scale Imaging of Articular Cartilage

PhD Student: Peter Del-MansoSupervisors: Andrew Todd-Pokropek (UCL), Martin Fry (UCL), Amaka Offiah (GOSH) Collaborators: Tryphon Lambrou (UCL), Bailiang Chen (UCL)Funding: EPSRC DTA

In the early stages of osteoarthritis, changes occur in the cartilage extracellular matrix, where a loss of proteoglycans and changes to the network of collagen fibrils are observed [1]. This occurs before any morphological changes to the cartilage. MRI techniques have shown promise in evaluating the constituents of the cartilage matrix.The concentration of both collagen and proteoglycans differ throughout the depth of the tissue, along with the orientation of the collagen fibrils. Articular cartilage is thin, resulting in a significant partial volume effect (PVE), with the image resolution limited by field strength. The PVE results in the unresolved averaging of the depth dependant constituent concentrations.Suggested MRI acquisition techniques include T1ρ mapping which has been shown to be sensitive to proteoglycan concentration [2], and T2 mapping [3] which has been shown to be sensitive to the orientation of the collagen fibrils. Diffusion tensor imaging has also been shown to be sensitive to the orientation of the collagen [4]. Techniques for the clinical assessment of cartilage matrix integrity are yet to be developed.A combination of acquisition sequences are likely to be required to provide methods for analysis, resulting in the need to develop methods by which information from a range of imaging methods and/or modalities can be combined. We believe that knowledge of the interactions between matrix components, their distribution and orientation must be incorporated into the analysis, as part of a multi-scale imaging technique to relate observations at a clinical scale to those obtained at the microscopic level. Our proposed multi-scale imaging framework utilises mathematical modelling techniques to relate the microscopic structure of cartilage to images obtained at clinical resolution, providing a clinically applicable technique to diagnose disease and monitor its progression.

References1. Buckwalter JA, Mankin HJ. Articular cartilage .2. Degeneration and osteoarthrosis, repair, regeneration, and

transplantation. Journal of Bone and Joint Surgery-American Volume 1997;79A(4):612-632.2. Duvvuri U, Kudchodkar S, Reddy R, Leigh JS. T-1 rho relaxation can assess longitudinal proteoglycan loss from

articular cartilage in vitro. Osteoarthritis and Cartilage 2002;10(11):838-844.3. Glaser C. New techniques for cartilage imaging: T2 relaxation time and diffusion-weighted MR imaging. Radiologic

Clinics of North America 2005;43(4):641-653.4. Filidoro L, Dietrich O, Weber J, Rauch E, Oerther T, Wick M, Reiser MF, Glaser C. High-resolution diffusion

tensor imaging of human patellar cartilage: Feasibility and preliminary findings. Magnetic Resonance in Medicine 2005;53(5):993-998.

Level set snake algorithms on the foetal heart

PhD Student: Irving Dindoyal, Supervisors: Andrew Todd-Pokropek (UCL), Alf Linney (UCL)Academic Collaborators: Tryphon Lambrou (UCL), Jing Deng (UCL, UCLH) Funding: EPSRC and MRC as part of MIAS IRC

The foetal heart has very thin intra-chamber walls which are often not resolved by ultrasound scanners and may drop out as a result of imaging. In order to measure blood volumes from all chambers in isolation, deformable model approaches were used to segment the chambers and fill in the missing structural information. Three level set algorithms in the foetal cardiac segmentation literature (two without and, one with the use of a shape prior) were applied to real ultrasound data. The shape prior term was extracted from the shape prior level set and incorporated into the amorphous snakes for a fairer comparison. To our knowledge this is the first time these existing foetal cardiac non shape based segmentation algorithms have been modified for shape awareness in this way [1]. In all cases the segmentation accuracy improved dramatically but the original template initialized shape prior level set did not necessarily give a better segmentation than the seed initialized snakes.

[1] Dindoyal I, Lambrou T, Deng J, Todd-Pokropek A, “Level set snake algorithms on the fetal heart”, ISBI From Nano to Macro, Washington DC, 864-867, April 2007.

[2] Lassige TA, Benkeser PJ, Fyfe D, and Sharma S, “Comparison of septal defects in 2D and 3D echocardiography using active contour models”, CMIG, vol. 24, no. 6, 377-388, 2000.

[3] Dindoyal, I., Lambrou, T., Deng, J., Ruff, C. F., Linney, A. D., Rodeck, C. H., and Todd-Pokropek, A. Level set segmentation of the fetal heart. Frangi, A. F., FIMH Barcelona, Spain,. June 2005.

[4] Dindoyal, I., Lambrou, T., Deng, J., and Todd-Pokropek, A. Fully automated shape prior applied to the fetal heart - preliminary results. 2006. Manchester. MIUA 2006. 4-7-2006

Example segmentation results. Without shape prior term (top row) and with shape prior term enabled on the bottom row. Manual tracings are in grey. The far right shows the affine registered prior to the image.

Passive Catheter Tracking Using MRI

PhD Student: Jenny EdgarSupervisors: David Atkinson (UCL) Michael Hansen (UCL/CH)Academic Collaborators: Michael Hansen (ICH UCL)Funding: EPSRC Doctoral Training Account

Cardiac catheterisation is currently carried out under X-ray guidance. The high exposure to ionising radiation required for these interventions greatly increases the risk of cancer to the patient, therefore we hope to develop a novel method for tracking the catheter during an intervention using MRI. There are two main techniques for tracking catheters using MRI; active methods (where coils are placed onto the catheter) and passive methods (where no coils are used). Active catheters have associated safety risks due to heating of the coils, and passive catheters generally produce a signal void which is difficult to visualise on projection imaging. We are trying to develop a new method of catheter tracking using substances with restricted diffusion. When diffusion imaging is applied the signal from these substances should remain visible, while signal from the body is suppressed. The substance being used is Tween-20, which forms micelles – this substance is a liquid which can easily be injected into the lumen of a cardiac catheter. I have been predominantly working on MRI pulse sequence development and MRI image reconstruction (both of which are programmed in C++). I have developed fast, diffusion weighted pulse sequences, and I am very lucky to have access to a 1.5T Siemens MRI scanner at Great Ormond Street Hospital twice every week in order to test these sequences. This method of passive catheter tracking has shown a lot of potential within phantoms, however we experience more problems when testing this method on the chest of a volunteer. We are currently investigating these problems, along with other applications for the fast imaging sequences developed.

Diffusion Imaging and Convection-enhanced Delivery

Research Associate: Hubert FonteijnPrincipal Investigator: Daniel AlexanderAcademic Collaborators: Professor Steven Gil ( Neurosurgery, Frenchay Hospital, Bristol), Ed White (Neurosurgery, Frenchay Hospital, Bristol), Professor Richard Kerswell (Department of Mathematics, University of Bristol)Funding: North Bristol NHS Trust

The treatment of many diseases of the brain, such as Parkinson and brain tumors, is severely limited by the Blood-Brain barrier, which prevents many drugs entering the brain. Convection-enhanced delivery (CED) is a technique which circumvents this barrier by directly injecting the drugs into the brain’s parenchyma. More specifically, if the drug is injected with a certain pressure gradient in a site in white matter, it is expected to start flowing along the white matter fibres to the site of the disease. This method is shown to be very promising, but can only be effective if the site of delivery can be well predicted from the site of injection. A good knowledge of the local and global micro architecture of the brain’s white matter is one of the critical factors for these predictions. The aim of this project is therefore to develop diffusion imaging methods which can facilitate these predictions.White matter is generally thought to consist of an intracellular space (axons and glial cells) and an extracellular space. The first part of this project will focus on characterizing the extracellular space, because this is the space in which the drug molecules are injected. The aim will be to determine critical micro structural parameters from diffusion imaging which will then be used in simulations of the fluid dynamics of the drug molecules.The second aim of the project is to develop a population-based atlas of probabilistic fibre connectivity. The aim of this part is that the neurosurgeon can interactively investigate which parts of the brain drug molecules on average reach when injected in a certain part of white matter. This will be derived from an atlas of fibre tracking results for each voxel in the brain’s white matter of a substantial number of subjects.

Example of a preliminary version of the interactive connectivity atlas based on probalistic tractography data. The arrow points indicates the seed voxel.

Electrophysiology Platforms for Image Guided Arrhythmia Management

Research Associate: Gang GaoPrincipal Investigator: Derek Hill (UCL): Co-Investigator: Simon Arridge (UCL)Academic Collaborators: Kawal Rhode (KCL), Reza Razavi (KCL)Industrial Collaborator: Philips Medical SystemsFunding: Technology Strategy Board

This project, which is a collaboration between UCL and KCL, aims to devise better techniques for treating patients with heart-rhythm abnormalities. It is becoming increasingly common for patients with heart-rhythm abnormalities (arrhythmias) to undergo procedures to cure them, rather than just take medicines to manage their symptoms. The procedures involve inserting plastic tubes called catheters into the heart, and then passing through those catheters specialized devices that can localize the abnormal conductive pathways in the heart muscle and ablate them. These procedures are normally carried out using x-rays to visualize the catheters, but x-rays do not clearly show the anatomy of the heart. In this project we will integrate anatomical information from other types of scan (MRI, CT) with the x-ray images collected during the procedure to provide better anatomical detail, and hence improve the success rate and reduce the likelihood of side-effects from these procedures. We are working closely with Philips Medical Systems to develop this technology and integrate it with commercial x-ray equipment.

(a) (b)Cardiac MR volume was overlaid onto the X-Ray images by using (a) the strategy developed in EPIGRAM and (b) by using clinical expert registration.

Monte-Carlo simulation framework for diffusion MRI

Research Associate: Matt Hall Principal Investigator: Daniel Alexander Funding: EPSRC

Diffusion MRI has the potential to provide important insights into the structure of the brain and to aid clinical diagnosis and surgical planning. An important step in bringing diffusion MRI into routine clinical use is to validate the techniques to assess their accuracy, precision and specificity. Validation is a difficult challenge due to the complexity of tissue and the way in which the tissue effects measurements made. The basic problem is the need to compare what is measured with a known ground truth. Short of dissecting patients brains post scan (which is rarely possible), techniques can be validated against phantoms – known structures constructed artificially and then placed in the scanner.My work compliments the physical phantom approach by using detailed computer simulations of diffusion in an environment that mimics the structure of brain tissue to synthesise diffusion MRI data. This also provides the ground-truth required by validation studies, but also allows the tissue model to be changed more subtly than would be possible in a phantom. The simulation approach also allows different types of scan acquisition to be tested with different analysis techniques to help assess the best way to acquire data in order to measure a particular feature. We have already found that prolonging diffusion gradients in the scan allows for better assessment of the dominant fibre direction in white matter. We have also constructed a model of swelling tissue to approximate the conditions in the immediate aftermath of Acute Ischemic Stroke (see figure).We are currently working on incorporating tissue models based on microscopy images in order to attempt to capture biological structures in more detail to see if and how this might effect the results of diffusion MRI analysis.

[1] Hall MG & Alexander DC, “Convergence and parameter choice for Monte-Carlo simulations of diffusion MRI”, submitted to IEEE Transactions on Medical Imaging (2008)

[2] Hall MG & Alexander DC, “A hexagon is a circle” ISMRM 2008 abs. num. 1779[3] Hall MG & Alexander DC, “Agreement & Disagreement in two models of Synthetic Diffusion MR data” ISMRM

2008 abs 1780[4] Hall MG & Alexander DC, “Finite pulse widths improve fibre orientation estimates in diffusion tensor MRI” ISMRM

2006 abs 1042[5] Hall MG & Alexander DC, “A simulation framework for diffusion MRI”, MIUA 2006

Fig: Tissue model in the aftermath of acute ischemia. Tissue is modelled as parallel cylinders that swell and squash into each other (top-left to bottom-right).

Detecting Change in X-ray Mammography and Digital Breast Tomosynthesis via Model Based Registration

Research Associate: John HipwellPrincipal Investigator: David Hawkes (UCL) Co-Investigator: Simon Arridge (UCL)Academic Collaborators: Christine Tanner (UCL) Yassir Jaffar (UCL)Industrial Collaborators: Carestream Ltd

Radiologists routinely compare x-ray mamograms to determine the presence or classification of breast cancer. Digital Breast Tomosynthesis (DBT) has potential to enhance the diagnostic accuracy of this task by eliminating the overlying features which confound conventional 2-dimensional x-ray mammography. Comparison of images acquired using either modality, however, is complicated by differences in the position of the highly deformable breast, as well as differences in the acquistion parameters such as x-ray exposure (for conventional x-ray) or number of acquired projections and hence out-of-plane resolution (for DBT). These factors may limit the accuracy with which corresponding regions of the breast can be identified, generate differences between images which are falsely identified as malignant, or obscure real changes in the underlying breast tissue.The aim of this research is to develop novel image registration algorithms specifically tailored to these modalities using knowledge of plausible mammographic compressions learned from finite element (FE) models of the breast [1].Key Findings: We have developed a novel validation technique for x-ray mammogram registration which uses real MR breast images [2]. We have also created a model of the x-ray acquisition process which enables realistic x-ray simulations from FE deformed MR images to be generated.Choosing a variety of different breast anatomies, as seen in MR, ensures that population variation is represented in the resulting cohort of simulated mammograms. By applying known compressions at a range of orientations we reproduce plausible deformations of the breast which might have occurred during mammography on separate occasions. Projections of these known deformations can then be used to compute the accuracy of registration algorithms.We are using this approach to evaluate conventional non-rigid registration techniques as well as developing new algorithms which will be able to distinguish 3D movement of breast tissue, between two x-ray or DBT mamograms, from changes in the mass of glandular tissue.

[1] C. Tanner, J. H. Hipwell, D. J. Hawkes. Statistical Deformation Models of Breast Compressions from Biomechanical Simulations, to appear in Proc. Int. Workshop on Digital Mammography, 2008.

[2] J. H. Hipwell, C. Tanner, W. R. Crum, J. A. Schnabel, D. J. Hawkes. A new validation method for validation of x-ray mammogram registration algorithms using a projection model of breast x-ray compression, IEEE Transactions on Medical Imaging, 26(7): 1190-1200, 2007

Figure Caption: Detail of a diagnostic mammogram (left) showing a mass, lower-left of centre. Existing non-rigid registration techniques provide an estimate of a 2-dimensional transformation field (center) which aligns the 5 year prior image (right) with the diagnostic image. We are developing new techniques which will provide a estimate of the 3-dimensional deformation of the breast.

3D dynamic reconstruction techniques for image guided interventions

Research Associate: Mingxing HuPrincipal Investigator: David Hawkes Co-investigator: Graeme PenneyAcademic Collaborators: Daniel Rueckert, Philip Edwards, Fernando Bello (Imperial College London), Roberto Casula (St. Mary Hospital), Steve Halligan, Shonit Punwani, Stuart Bloom (UCLH)Industrial Collaborators: Medicsight

How to use the optical cameras to provide more 3D information for preoperative and intraoperative procedures is one of the challenging research areas in image guided interventions, especially for minimally invasive surgery. My current research work focusses on addressing some of these challenges with the computer vision technologies, including 3D/4D reconstruction and registration, feature analysis, information fusion, stereo reconstruction and dynamic mosaic. We developed a new 3D reconstruction technique for the heart surface, this technique can provide a wider field-of-view with 3D information for surgeons, including recovering of the missing data. By using this new technique, the reconstructed surface can cover a large area of the heart and this helps to solve the fundamental problem of minimal invasive surgery, that is, the narrow field-of-view. We also developed a novel heart motion analysis technique using moving cameras only. Unlike the traditional methods, in which cameras need to be fixed, our cameras can move around the operation scenes, which is a big advantage for intro-operative surgery. With this robust technique we can convert the complicated 4D (3D+T), the almost impossible, dynamic reconstruction and registration problem back to 3D problem.These techniques can be applied to a range of minimally invasive surgeries, such as coronary artery bypass surgery, liver surgery and colonoscopy.

[1] M.X. Hu, K. McMenemy, S. Ferguson, G. Dodds and B.Z. Yuan, “Epipolar geometry estimation based on evolutionary agents”, Pattern Recognition (Elsevier), Vol. 41, No. 2, pp. 575-591, 2008.

[2] M.X.Hu, G P. Penney, P. J. Edwards, M. Figl, D. J. Hawkes, and D. Rueckert “A novel method for heart motion analysis based on geometry estimation”, 11th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2008), (accepted)

[3] M.X.Hu, G P. Penney, P. J. Edwards, M. Figl, D. J. Hawkes, “3D Reconstruction of Internal Organ Surfaces for Minimal Invasive Surgery”, 10th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2007), Part I, LNCS 4791, pp. 68-77.

Prostate Gland Motion for Image-guided Interventions

PhD Student: Yipeng HuSupervisors: Mark Emberton and Hashim Uddin (Institute of Urology, UCL), Doug Pense and Mahua Sahu (National Medical Laser Centre, UCL), Clare Allen (Dept. of Radiology, UCL)Funding: EPSRC

Image registration is important in image-guided medical interventions because it enables diagnostic quality, preoperative images to be used for intraoperative guidance. A common task is registering such images to relatively poor quality images, such as 3D ultrasound which can be obtained during an intervention. Furthermore, organ motion often occurs between imaging before an intervention and imaging within an intervention. Quantifying, predicting and compensating for organ motion and physical changes in response to therapy (such as swelling) remain some of the most important challenges in this field of research. I am interested in developing practical methods which can be used in inter-modality image registration problem during ultrasound-guided prostate interventions. One method currently under development involves combining a statistical approach and finite element analysis to build a 3D patient-specific statistical motion models (SMMs) of the prostate gland. This method is accurate (landmark positions are predicted within 2mm [1,2]) and fast. My ultimate aim is to embed this technique within a real-time deformable registration algorithm, which can be used in a clinical environment.

References:[1] Y. Hu, D. Morgan, H. Uddin Ahmed, D. Pendsé, M. Sahu, C. Allen, M. Emberton, D. Hawkes, and D. Barratt.

Modelling Prostate Gland Motion for Image-guided Interventions. To appear in Proc. ISBMS08 (LNCS series, Springer).

[2] Y. Hu, D. Morgan, H. Uddin Ahmed, D. Pendsé, M. Sahu, C. Allen, M. Emberton, D. Hawkes, and D. Barratt. A Statistical Motion Model based on Biomechanical Simulations for Data Fusion during

The figure shows a prostate model fitted to 3D transrectal ultrasound images. The gland surface is shown as a white mesh, whilst the urethra is shown as a green tubular structure. Ultrasound slices are also shown with displacement field predicted by the SMM.

A Model-based Approach to Comparing Breast Images

PhD Student: Yassir JaffarSupervisors: David Hawkes (UCL) Academic Collaborators: John Hipwell (UCL)Funding: EPSRC

Breast cancer is the most common form of cancer in women. Mammography is currently the gold standard of non-invasive techniques for reliable detection of early, non-palpable and potentially curable breast cancer. A mammogram is acquired by compressing the patient breast between two acrylic plates and irradiating the breast with X-Rays, the transmission of which are recorded either on film or by a digital detector. The resulting image reveals the anatomy of the breast by relating the contrast at each point to the total X-Ray attenuation, and hence tissue density, encountered by the transmitted X-Ray photons. Among the features highlighted by a mammogram are normal tissue, vessels, pectoral muscle, different types of benign or malignant masses and calcifications. Each type of mass has different properties of shape, size, distribution and brightness which help the radiologist to effectively diagnose the breast tumor. This means that the experience of the radiologist and image quality are the main important factors in successful manual classification of this complex task.Given the projective nature of the image of the breast under different compression levels, one of the main interests at present is to investigate the stability or integrity of features across these different compression levels for a particular set of simulated mammograms. This will present an insight into how these features behave under compression and lead to novel registration algorithms. These algorithms will align a pair of mammograms by considering the formulation of new features under compression as a result of superimposition and occlusion of the overlying breast tissue.

Mammograms of a patient who self-referred with a palpable mass. Although an ultrasound guided biopsy was performed, second reading of the mammograms concluded there were no suspicious lesions present. Interpreting and comparing X-Ray mammograms is a complex task due to the projective nature of the images.

Iterative Deconvolution of Simultaneous Dual Radionuclide Projections for CdZnTe Based Cardiac SPECT

Research Associate: K Kacperski, Academic Collaborators: K Erlandsson, S Ben-Haim, D Van Gramberg, B F HuttonFunding : Spectrum Dynamics, Israel; 3 years; Aug 2008; £200k

We propose a method of separating primary 99mTc and 201Tl photons from projections acquired on a dedicated cardiac system (D-SPECT, Spectrum Dynamics, Israel) based on CdZnTe detectors during a simultaneous dual radionuclide rest-stress myocardial perfusion scan. The main difficulty in such procedure is the crosstalk between the two radionuclides, primarily due to down-scatter of the 99mTc photons into the main 201Tl energy window. CdZnTe detectors offer much better energy resolution as compared to the standard scintillators, allowing use of narrower energy windows and therefore reduced down-scatter. On the other hand, spectral characteristics of the pixelated solid state detector is different from that of a large crystal scintillator, in particular low energy tails below main photopeaks are present. Consequently, standard scatter correction methods e.g. based on subtracting scatter counts measured in adjacent narrow windows are not directly applicable. Our method consists of constructing a model of spectral and spatial distribution of counts in the measured projections and solving the obtained set of equations for the pure primary counts using the Maximum Likelihood Expectation Maximisation iterative algorithm. The deconvolved primary count projections are then reconstructed using the standard OSEM algorithm. The method was validated using scans of a thorax phantom with cardiac inserts and fillable defects. The contrast of the 201Tl defect improved by almost a factor of 2 after applying the iterative deconvolution, without noticeably increasing image noise (see fig. below). We also applied the method on several patients’ data observing significant reduction in scatter background on 201Tl images and, in some cases, potential change in diagnosis after correction. The proposed approach is fast and independent of the reconstruction algorithm. It can be applied to other pairs of radionuclides e.g. 99mTc and 123I, or as a scatter correction method for single radionuclide studies.

Reconstructed images of the phantom scan: short axis slices, horizontal long axis slices and polar map for reconstructions based on raw versus deconvolved projections.

Evaluation of hypoxia in head and neck tumours using Copper-64 ATSM

Research Associate: Tryphon LambrouPrincipal Investigator: Michele Saunders(UCL Cancer Institute) Academic Collaborators: John Dickson, Irfan Kayani, Wendy, Waddington, Peter Ell, Brian Hutton (UCLH)Funding: Cancer Research UK

Hypoxia, or low oxygenation, has emerged as an important factor in tumour biology and response to cancer treatment, since it can promote tumour progression, and failure of radiotherapy (RT) due to the increased radioresistance of hypoxic cells compared with normally oxygenated cells. It has been correlated with angiogenesis, tumour aggressiveness, local recurrence and metastasis, and it appears to be a prognostic factor for several cancers, including those of the cervix, head-and-neck cancers (HNC), prostate, pancreas, and brain.

The objectives of this project include:

Validation of 64Cu-ATSM PET scanning as an imaging modality for identifying areas of hypoxia in individuals with squamous cell carcinoma of the head and neck.Feasibility of 64Cu-ATSM PET scanning in the clinical setting to identify subvolumes within tumours amenable to dose escalation. Identifying a tumour to muscle uptake ratio (T/M) ratio or a standardised uptake value (SUV) to define a region of hypoxia. Correlating anatomical boundaries of hypoxia on PET scan with histology.

This project raises challenges in terms of: i) image analysis (PET/CT to stained histology image registration 2D to 3D, or 3D to 3D), ii) pharmaceutical kinetics analysis, trying to determine the impact of tumour blood flow on 64Cu-ATSM distribution.

CT PET PET/CT

UK’s first 64Cu-ATSM PET/CT scan for imaging hypoxia of HNC.

Use of imaging to assess efficacy and safety of new treatments for Alzheimer’s Diseases (AD)

Research Associate: Kelvin LeungPrincipal Investigator: Sebastien Ourselin (UCL) Co-Investigator: Nick Fox (UCL)Academic Collaborators: Jo Barnes (UCL), Marc Modat (UCL),Matt Clarkson (UCL)Industrial Collaborator: Ixico LtdFunding: Technology Strategy Board

The aims of the project include devising novel imaging biomarkers for the assessment of the efficacy of drugs to treat AD, developing automated methods of detecting and measuring new white matter lesions from serial T1 volumes, detecting change in inflammation using T2 or T2* volumes to assess side effects of new treatments, investigating the robustness of the algorithms for quantifying brain atrophy measurements with respect to imaging artefacts as well as sensitivity to user-interaction such as interactive segmentations. He will be working on new methods for the automation of image measurements such as the Boundary Shift Integral, and the calculation of hippocampal volume and the use of non-rigid registration to assess longitudinal changes in grey matter, white matter and ventricles.Additionally, Kelvin is working on a dedicated medical image analysis platform for widespread use within the university and beyond. This platform will provide a plugin architecture to provide easy extension points, and in time will become the de facto working environment for all imaging research and trials across CMIC and DRC. The platform will bring together the expertise from many years experience in image registration, segmentation, image quantification and visualisation, leading to increased cross site collaboration and higher throughput.

Quantification of blood flow from rotational angiography

Optimal design for a human brain SPECT system based on slit-slat collimation and a high resolution detector

PhD student: Shelan Mahmood Supervisors: Brian Hutton, Ian CullumAcademic Collaborators: K Erlandsson (Institute of Nuclear Medicine, UCL)Funding: EPSRC and GE Healthcare

We have designed a slit slat collimator system to be used with a novel silicon drift diode (SDD) detector with 1mm intrinsic resolution for clinical brain SPECT. The detector is in the process of being built by an active EC project (HICAM) and should be available for testing at our centre at the beginning of next year. We aim to optimize the design of the slit-slat collimator system to provide a system resolution of around 6mm in both dimensions and an efficiency exceeding that of a parallel-hole collimator. A slit-slat collimator is in a sense pinhole in the transaxial dimension and parallel-hole in the axial dimension. As is the case with pinhole, slit-slat provides high resolution and sensitivity that improves with reduction in distance to the object. System sensitivity can also be increased by increasing the number of slits. However, the number of slits/pinholes that can be included in a system is currently limited by the degree of overlap of the projections on the detector plane (known as multiplexing). Multiplexing has been shown to reduce image quality due to the uncertainty regarding the origin of the projections. We have shown that multiplexing can be used to increase system sensitivity and therefore reduce noise in the image quality provided that mixed multiplexed (MXD) and non-MXD data are acquired. This has positive implications for future system design where multiplexing can be used to increase system sensitivity without sacrificing noise.

Figure 1: Slit-slat collimator-detector system

A Continuous 4D Motion Model from Multiple RespiratoryCycles for use in Lung Radiotherapy Research Associate: Jamie McClellandPrincipal investigator: David Hawkes(UCL) Academic Collaborators: Simon Hughes (UCLH) David Landau (GSTT)Funding: Department of Health

Respiratory motion is a major factor contributing to errors and uncertainties in Radiotherapy (RT) treatment of lung tumours. Knowledge of this motion may improve the planning and delivery of RT treatment for lung cancer patients. We have developed and evaluated methods of building patient specific respiratory motion models. These relate the internal motion to an external surrogate signal that can be measured during data acquisition and treatment delivery. The models offer a number of advantages over current methods of imaging and analysing respiratory motion, in particular their ability to account for variations in the respiratory motion.To construct the models Computer Tomography (CT) data is acquired over several respiratory cycles to sample some of the variation in the respiratory motion. B-spline non-rigid registrations are used to recover the motion and deformation from the CT data. The models are then constructed by fitting functions that relate the registration results to the respiratory signal. Initially models were constructed that related the registration results to a single parameter, the phase of the respiratory cycle, and averaged out any variation in the respiratory motion. More recently models have been constructed that relate the registration results to two respiratory parameters, with the intention of modelling some of the variation in the respiratory motion. Our results show that the models can predict the respiratory motion in the CT data very accurately (mean error < 1.4 mm).

Correction of respiratory motion in 82-Rb cardiac studies acquired with respiratory-gated PET and breath-hold CT

PhD student: Sarah J. McQuaidSupervisors: Brian Hutton (UCL), Vincent Cunningham (GlaxoSmithKline)Academic Collaborators: J Dey (Umass), T Lambrou (UCL)Industrial Collaborators: GlaxoSmithKlineFunding: BBSRC and GlaxoSmithKline.

Respiratory motion during 82-Rb cardiac PET-CT imaging causes blurring and can introduce attenuation-correction artefacts if mismatches between PET and CT arise. The purpose of this project was therefore to characterise such artefacts and overcome them by developing a method of motion-correction. By simulating attenuation-mismatches using a digital phantom, it was demonstrated that mismatches in both the heart and the diaphragm positions could produce clinically significant artefacts in the reconstructed image [1]. Motion-correction algorithms must therefore consider the motion of these organs in order to produce quantitatively accurate results.Robust motion-correction methods have yet to be established in the case where a single CT is acquired, due to challenges in performing a respiratory-matched attenuation-correction. This is because attenuation maps need to be derived over the respiratory cycle and the only motion information available is from the noisy and anatomically-limited gated-PET images. This suggests that a model could be valuable in complementing the information obtainable. A Statistical Shape Model of the diaphragm has therefore been constructed (see Fig. 1), with initial results indicating that this technique could assist in identifying the diaphragm position, by fitting only a few parameters. The model has been built by segmenting the diaphragm in a set of respiratory-gated CT datasets [2] and then performing Principal Component Analysis on corresponding landmarks. By applying such a model to gated PET data, this should not only enable a respiratory-matched attenuation-correction, but also provide the motion parameters to facilitate the subsequent motion-correction.

[1] S. J. McQuaid, et al., Eur. J. Nucl. Med. Mol. Imaging., vol. 35(6), 1117-1123, 2008.[2] S. J. Martin, et al., Nucl. Sci. Symp. Conf. Rec., 2680-2685, 2007.

Fig. 1. The effect of changing the weightings of the first 6 modes of variation on the mean diaphragm shape at full-inhale.

Image Registration in the Presence of Contrast Variation and Motion Corruption

PhD Student: Andrew MelbourneSupervisors: David Hawkes (UCL), David Atkinson (UCL)Academic Collaborators: Martin Leach, David Collins (ICR), Mark White (UCL)Funding: EPSRC

Registration of the dynamic contrast-enhanced magnetic resonance images (DCE-MRI) used for analysis of the vascular properties of soft tissue is difficult. Conventional image registration cost functions that depend on information content are compromised by the changing intensity profile, leading to mis-registration. The work in [1] describes a method for the separation of contrast-enhancement intensity changes from motion artefacts using a data-driven model of uptake patterns formed from a principal components analysis (PCA) of time-series data. The method requires neither segmentation nor a pharmacokinetic uptake model and can allow successful registration in the presence of contrast enhancement. Registration is performed repeatedly to an artificial time series of target images generated using the principal components of the current best-registered time-series data. The aim is to produce a dataset that has had random motion artefacts removed but long-term contrast enhancement implicitly preserved. The procedure is tested on 22 DCE-MRI datasets of the liver and more recently on DCE-MRI simulations using realistic pharmacokinetic parameters combined with an elastic deformation model. The work has also been applied to motion corrupted diffusion weighted MRI in which contrast-variations due to gradient direction are encoded in the PPCR algorithm [2] (Figure 1).

[1] Registration of dynamic contrast-enhanced MRI using a progressive principal component registration (PPCR). Melbourne, A. et al. Phys Med Biol, 2007, 52, 5147-5156

[2] Non-Rigid Registration of Diffusion Weighted MRI Using Progressive Principal Component Registration (PPCR). Melbourne, A. et al. Proceedings of ISMRM, 2008, 3097.

Figure 1. Fractional Anisotropy map calculated from 15 motion corrupted diffusion directions a) before correction b) after motion correction by PPCR.

Non-rigid registration including biomechanical constraints for longitudinal study of atrophy in Alzheimer’s disease

PhD Student: Marc ModatSupervisors: Sebastien Ourselin (UCL), Nick Fox (UCL), David Hawkes(UCL)Academic Collaborators: Jo Barnes (UCL), Zeike Taylor (UCL), Ged Ridgway (UCL)Funding: EPSRC

Non-Rigid Registration is a tool commonly used in medical analysis. However techniques are usually time consuming. Graphics Processing Units (GPUs) achieve a high floating point capacity by distributing computation across a high number of parallel execution threads. This computational capacity can be used to dramatically decrease the computation time of well-known algorithms provided they can be mapped to a parallel architecture. We have developed a parallel version of the well-known Free-Form Deformation algorithm and implemented it using the CUDA API from NVidia. Execution time falls from a few hours to less than one minute with similar accuracy.We are now developing new algorithms incorporating accurate and realistic biomechanical constraints. Our group recently developed an efficient GPU-based non-linear finite element solver which allows rapid simulation of soft tissue deformations. A registration algorithm driven by a biomechanical model is perfectly suitable for longitudinal studies where different time points are acquired for the same patient. This framework applied on Alzheimer’s disease patient scans allows the tracking of changes over time in the brain. Quantifying the rate of atrophy of the brain is currently done in clinical trials using labour-intensive method requiring semi-automatic segmentation and thus requires significant operator time. Using accurate and fast registration schemes dramatically decreases the labour and time spent to evaluate atrophy without decreasing the accuracy of the quantification.Another application of such a registration framework is the creation of 4-dimensional Alzheimer’s disease models based on existing data all related together with physical deformations

Regularized Super-Resolution for Diffusion MRI

PhD Student: Shahrum Nedjati-GilaniSupervisors: Daniel Alexander Academic Collaborators: Geoff Parker (University of Manchester)Industrial Collaborators: Philips Medical SystemsFunding: EPSRC

Diffusion MRI provides insight into the microstructural architecture of tissue by observing the restricted and hindered displacement of water molecules undergoing Brownian motion. Diffusion-Tensor MRI (DT-MRI) is the most common diffusion MRI technique and is often used for reconstucting fibre population information, such as fibre orientation. However, DT-MRI is only capable of recovering a single fibre orientation in each voxel. Other reconstruction algorithms exist that overcome this problem; however, these more complex algorithms have their own shortcomings. For example, they can not differentiate between fanning and bending structures, or correctly assign the spatial arrangement in voxels containing more than one fibre population. The aim of our work is to find and evaluate methods capable of identifying and distinguishing complex fibrous microstructure accurately in each voxel of a 3D diffusion MRI acquisition.

[1] S. Nedjati-Gilani, G. J. Parker, D. C. Alexander, “Regularized Super-Resolution for Diffusion MRI”, IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, p. 875-878, May 2008.

[2] S. Nedjati-Gilani, G. J. Parker, D. C. Alexander, “Regularized Super-Resolution for Diffusion MRI”, 16th Scientific Meeting of the International Society for Magnetic Resonance in Medicine, Toronto, ON, Canada, p. 41, May 2008.

[3] S. Nedjati-Gilani, G. J. Parker, D. C. Alexander, “Mapping the Number of Fibre Orientations per Voxel in Diffusion MRI”, 14th Scientific Meeting of the International Society for Magnetic Resonance in Medicine, Seattle, WA, USA, p. 3169, May 2006.

Regularized Super-Resolution for Diffusion MRI. In this figure, we have quadrupled the resolution in order to better differentiate between the corpus callosum (red) and the cingulum (green), and reduce partial volume effects in voxels containing both fibre populations (highlighted in the area in the yellow box). The length of the lines represent anisotropy, and the colour represents orientation

Semi-automatic method for the diagnosis of pulmonary embolism using parametric images derived from co-registration of lung perfusion and ventilation studies with single-photon emission computed tomography

PhD Student: Margarita NúñezSupervisors: Brian HuttonAcademic Collaborators: O Alonso, F Mut (University of the Republic, Montevideo), V Prakash(UCL)

The aim of the project is to develop a semi-automatic, semi-quantitative method for evaluation of lung perfusion and ventilation studies, which would help clinical decision by facilitating the interpretation of results. This should lead to a more accurate diagnosis of pulmonary embolism (PE), increasing both sensitivity and specificity of the procedure and minimizing operator’s interaction. Methodology includes performing lung perfusion and ventilation using SPECT. All data are to be corrected for attenuation and used to form parametric images depicting ventilation/perfusion (V/Q) mismatches which are characteristic of PE. Attenuation correction is performed through the acquisition of a scatter window to determine body and lung boundaries and the attenuation coefficient is derived from comparison with measured attenuation using SPECT/CT. The reconstructed SPECT data is reprojected to create ‘planar like’ images at any desired angle. This technique also allows contra-lateral lung to be removed and the medial view to be visualised. Normalisation of ventilation and perfusion data for subtraction is based on areas of identified ‘normal’ lung using intensity histograms. Correction for respiratory motion is also considered. The strategy is to perform a volumetric co-registration of perfusion and ventilation studies and to generate a 3-D parametric image which displays only the areas of V/Q mismatch. For anatomic reference, the abnormal areas will be superimposed to a template which delineates the organ boundaries and the limits of vascular segmentation. An automatic report will be generated including the presence, number, location and extension of mismatch defects and a probability of PE will be calculated based on an accepted interpretation criteria. For validation, a virtual NCAT phantom will be used and the method will be applied to a number of patients submitted for evaluation of possible PE.

Time-resolved whole-heart cardiac imaging using highly parallel magnetic resonance

Research Associate: Freddy OdillePrincial Investigator: David AtkinsonFunding: EPSRC

In the context of cardiac magnetic resonance imaging, patient motion is a major issue which requires, in standard imaging protocols, the use of specific acquisition techniques (synchronisation with the cardiac and respiratory cycle). However these methods are not optimal, as patient motion is not always reproducible, and as they require longer acquisition times. Therefore the inability to deal with complex motion imposes a trade-off between the desired image quality, in terms of spatial resolution and signal-to-noise ratio, and the acquisition time.Based on previous work [1-2], we aim for improved handling a complex motion by integrating a motion model accounting for non-rigid deformations into the reconstruction. This technique has been shown to allow free-breathing acquisitions to be reconstructed with significant reduction in motion artefacts. However, several aspects of the method still need to be improved.Our first objective is to study certain basic assumptions regarding the combination of these methods with parallel imaging (i.e. in the context of multiple coil acquisition). This requires studying the influence of patient motion on radiofrequency coil sensitivities, the propagation of these errors in reconstruction algorithms, and correcting for errors due to inaccurate coil sensitivities [3].Furthermore, we aim at combining these techniques with the fast imaging techniques developed by our collaborators at King’s College, such as navigator-based and self-navigated sequences, or optimised k-space trajectories (i.e. optimized sampling schemes). Validations will be performed with free-breathing scans from healthy volunteers, with applications to high resolution coronary imaging and 3D whole heart CINE imaging in particular.The computational efficiency of these algorithms is also under investigation. Initial work on parallel implementation, using GPU programming, has already allowed significant improvements in reconstruction times.

[1] Odille, F.; Cîndea, N.; Mandry, D.; Pasquier, C.; Vuissoz, P.-A. & Felblinger, J. Generalized MRI reconstruction including elastic physiological motion and coil sensitivity encoding. Magnetic Resonance in Medicine, 2008, in press.

[2] Odille, F.; C.; Vuissoz, P.-A.; Marie, P.-Y. & Felblinger, J. Generalized Reconstruction by Inversion of Coupled Systems (GRICS) applied to free-breathing MRI. Magnetic Resonance in Medicine, 2008, in press.

[3] Odille, F.; C.; Vuissoz, P.-A. ; Felblinger, J. & Atkinson D. Generalized Reconstruction by Inversion of Coupled Systems (GRICS) applied to parallel MRI. In Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on. IEEE, 2008, pp. 1019–1022.

Cardiac MR image (sort axis slice). Gold standard image (a); Parallel MRI reconstructions from inaccurate sensitivity maps: standard method (SENSE) (b), and proposed method with sensitivity map optimization (c)

3D Model Construction from Real Brain-Tissue Images

PhD Student: Eleftheria PanagiotakiSupervisors: Daniel Alexander (UCL) Academic Collaborators: Simon Julier (UCL)Funding: EPSRC

Diffusion MRI is a powerful non-invasive method that measures the scattering of particles, usually water molecules, in the living human brain. It has emerged as an important tool for inferring the structure of underlying white-matter and probing various pathologies. Despite the widespread application of diffusion MRI, there is still the need to validate its use as a method of measuring white matter architecture. Synthetic data can be used for testing and tuning of reconstruction methods based on diffusion MRI. Monte-Carlo simulation is a method of generating detailed synthetic diffusion MRI data by simulating the physical process. Current simulation techniques model brain tissue using simple geometry such as cylinders. By enhancing the visual realism and richness of data used to construct the geometric model, we hope to improve the accuracy of diffusion MRI simulation and have a better insight in the brain’s morphology. Our method aims to construct a model of brain tissue using Confocal Laser Scanning Microscopy images (CLSM). A confocal microscope creates sharper images of a specimen than when viewed with a conventional microscope. Apart from better observation of fine details, it also allows for three-dimensional reconstruction of a volume of the specimen. We perform the reconstruction by assembling a series of thin slices taken along the vertical axis of a small volume of white matter (e.g. corpus callosum, middle part of the truncus) and applying the Marching Cubes algorithm to produce a triangle mesh which represents the surface corresponding to the white matter architecture. Once an accurate surface is reconstructed, we will use the complex mesh model of the brain tissue as a substrate in the Monte-Carlo diffusion simulation. Our research will include testing the synthetic data from the complex model against other measurements in literature and finally comparing them to real measurements from similar tissue to directly determine the accuracy of our model. Future directions may include mechanisms for emulating pathological symptoms such as cell swelling and shrinking.

1] Hall MG & Alexander DC, “Convergence and parameter choice for Monte-Carlo simulations of diffusion MRI”, submitted to IEEE Transactions on Medical Imaging (2007)

[2] Hall MG & Alexander DC, “A simulation framework for diffusion MRI” Proceedings of Medical Image Understanding and Analysis, Manchester, UK (2006)

[3] Hall MG & Alexander DC, “Agreement and disagreement between two models of diffusion MR signal” ISMRM 2008

[4] Hall MG & Alexander DC, “A Hexagon is a circle” ISMRM 2008

Regularization in diffuse optical tomography with unregistered information theory priors

PhD student: Christos PanagiotouSupervisors: Adam Gibson, Simon ArridgeFunding: EPSRC

Diffusion optical tomography (DOT) aims to retrieve spatially and quantitatively accurate distributions of the optical characteristics in a medium of interest, usually expressed in terms of light absorption and scattering. As the major absorber of near infra red light is haemoglobin, the retrieved distributions have direct correlation with quantities of physiological importance such as blood flow, volume and also oxygenation levels of tissue in multi-spectral studies.Unfortunately the accuracy of the recovered solution is compromised due to the highly ill-posed nature of the underlying inverse problem. We propose the regularization of the solution process, via the introduction of a priori information from an alternative high resolution anatomical modality, using information theory concepts such as joint entropy and mutual information. Both functionals enable the evaluation of the similarity between the reconstructed image and a reference structural image, while they bypass the multi-modality barrier manifested as the incommensurate relation between the gray value representations of corresponding anatomical features found in both modalities. The reference image effectively enforces, at some extent, its structure to the optical image but does not affect the actual values of the recovered optical coefficients. In addition, in order to minimize the bias from possible misalignment between the corresponding structures in the solution and prior space we propose the recovery of the correct spatial location of the prior simultaneously with the optical image reconstruction process.

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Developing an inter-patient registration methodology for PET/CT FDG images of the head and neck as a step towards building an atlas of uptake patterns

PhD student: Yanni PapastavrouSupervisors: Brian Hutton, Dave Hawkes

Combined PET-CT FDG head and neck studies can be challenging to interpret due to complex anatomy, with many structures in close proximity. Both anatomy and FDG uptake can reflect normal variants or pathology. We have selected a set of studies that are normal in uptake and anatomy within this region, with a view to constructing an atlas by registering the images onto a common template. This is a step towards understanding patterns of normal FDG uptake in this region.B-spline based free form deformation (FFD) is used to register the combined PET-CT images to the template PET-CT image. Initial work utilising CT information during registration demonstrated that the correspondence improves as the control point spacing decreases. However, there are cases of large-scale registration errors that propagate to the finer scale.We wish to overcome instances of mis-registration at the coarse scale by utilising information from all available sources. We seek a multi-component similarity measure that utilises the best combination of information from both the PET and the CT images to improve the robustness of the registration at the coarse scale. Similarity measure characterisation plots are used to compare rival similarity measures. We do this by applying various FFD transformations of varying amplitude to the dual-intensity PET-CT image and evaluating the similarity measure relative to the original. We then compute the similarity measure whilst the FFD vector field was multiplied by a scalar amplitude that varies between zero and one, the maximum warp. The features of this plot are used to investigate the properties of each similarity measure.Once the robustness for the coarse-scale registration has been improved, the fine-scale registration shall be used to achieve correspondence across the set of images for the structures within which the FDG uptake patterns can then be assessed.

Image: Inter-patient spine alignment demonstrated by overlay images: Target is rendered in green and the transformed image in grey colour-scale. The vector field is also overlaid.

Voxel-wise Statistical Analysis of Longitudinal MR Imaging of Dementia

PhD Student: Ged RidgwaySupervisors: Sebastien Ourselin (UCL), Nick Fox (UCL), Derek Hill( UCL)Academic Collaborators: Jason Warren (UCL)Industrial Collaborator: Brandon Whitcher (GlaxoSmithKline)Funding: EPSRC and GlaxoSmithKline

The goals of this project are to further the methodology of automated computational neuroanatomy, and to apply the methods to serially acquired MR images of patients suffering from dementia. The hope is to advance understanding of disease progression and improve chances of early detection.Key contributions are: improved preprocessing strategies for longitudinal Voxel-Based Morphometry; development of efficient statistical software allowing voxel-wise permutation testing of multivariate data with powerful step-down multiplicity correction; investigation of multivariate measures such as strain tensors, derived from non-rigid registration of serial image sets; combining longitudinal bias correction and non-rigid registration to their mutual benefit; exploring the potential of automatic image-based classification approaches such as Support Vector Machines.The figure shows example results from a comparison of 36 probable Alzheimer’s Disease patients with 20 age- and gender-matched controls.Non-rigid registration between baseline and 12-month repeat MR images, and from baseline images to a standard template, produced standard-space voxel-wise measurements characterising structural brain changes over time. Overlaid on coronal and axial slices through the study-specific mean image are statistically significant (pFWE<0.05) results from two complementary multivariate measures: left, the three components of the longitudinal deformation field; right, the six unique elements of the Hencky strain tensor.

Selected first-author publications[1] Longitudinal Voxel-Based Morphometry with Unified Segmenation: Evaluation on simulated Alzheimer’s disease.

MIUA 2007 [2] Longitudinal Multivariate Tensor- and Searchlight-Based Morphometry Using Permutation Testing. OHBM 2008[3] Ten simple rules for reporting voxel-based morphometry studies. NeuroImage 2008

Electroanatomical Fusion for Guiding EP Procedures and Patient Specific Modelling

PhD Student: Yuan RuanSupervisors: Simon Arridge (UCL), Derek Hill (UCL) Industrial Collaboraors: Philips Medical System, IRIAFunding: EPSRC

Techniques relating the electrical potential distributions among the heart surface (both Endocardium and Epicardium) and the body surface include Catheter technique, Angioplasty Balloon Catheter Inflation technique, electrocardiography (ECG) and Body Surface Potentials Mapping technique. They have been studied and improved in clinical applications to assist with both diagnosis and intervention of pathological heart conditions. The treatment of arrhythmia is an important field in which those techniques are used. In current clinical practice localization of the origin of arrhythmia is achieved by the traditional catheter techniques. Where the abnormal electrical focus or pathway is ablated, a procedure inserting an electrical measurement catheter into the patient’s heart chambers will be performed to find the best ablation location prior to interventions (radio-frequency ablation). An important issue is that this conventional procedure is still invasive, thus non-invasive techniques which can accurately relate heart and body surface electrical activities are preferable in clinical applications and are likely to replace the invasive measuring system in the future. However, problems still exist in the present non-invasive techniques, for example the accuracy can not be guaranteed due to ignoring inhomogenities or anisotropy in living human tissue. Effective conductivities values measured in literature varies from individuals and experiments, thus directly using literature values does not lead to satisfactory clinical cases. In the inverse electrocardiology study, such parameters together with potential distributions on the heart may expect to be accurately evaluated only knowing electrical potential distribution on the body; thereby we could make current clinical procedure less invasive and also further understand the working principle of the heart.

Shape and contrast recovery in optical tomography with level sets

Research Associate: Martin SchweigerPrincipal Investigator: Simon Arridge Funding: EPSRC

Optical tomography is an imaging method that recovers the spatial distribution of optical parameters in a scattering medium from boundary measurements of light transmission. Many applications of optical tomography in medical diagnostics, including the imaging of haematoma and tumours or the localisation of organs marked by a contrast agent, require the detection of well-defined boundaries between a homogeneous or weakly varying background and inclusions of different optical parameters. Conventional reconstruction methods which seek to recover intensity values for each voxel in the image are often not able to recover sharp edges, because the low spatial information content of data acquired from optical tomography instrumentation leads to blurring of boundaries of features and loss of contrast.A better alternative for this type of imaging problem are shape-based reconstruction techniques, such as level sets, because they inherently enforce the preservation of edges in the image. We are developing a level set technique for the simultaneous recovery of the shape, location and contrast of absorbing and scattering objects included in a three-dimensional medium[1,2]. It defines an forward model of diffuse light transport, and iteratively seeks to minimise an objective function expressing the difference between model and measurement data. At each iteration, the gradient of the objective function with respect to the optical parameter distribution is used to update level set functions. The zeros of the level set functions in turn define the estimates of the object boundaries.We have compared images reconstructed from simulated frequency-domain boundary measurements to a voxel-based conjugate gradient method. The results demonstrate that in the presence of high-contrast target objects the level-set approach yields better results than a conventional reconstruction of voxel-based parameter values.

1] M. Schweiger, S. R. Arridge, O. Dorn, A. Zacharopoulos and V. Kolehmainen, “Reconstructing absorption and diffusion shape profiles in optical tomography by a level set technique”, Opt. Lett. 21(4), 471-473 (2006).

[2] M. Schweiger, O. Dorn and S. R. Arridge, “3-D shape and contrast reconstruction in optical tomography with level sets”, J. Phys.: Conf. Ser. (in press)

Cross sections of target and reconstructions of objects embedded in a 3D volume. Columns from left to right: target images, voxel-based reconstruction, level-set reconstruction. Top row: absorption images, bottom row: diffusion images.

Exploiting the fibre-orientation distribution for probabilistic tractography

PhD Student: Kiran SeunarineSupervisors: Daniel Alexander (UCL)Funding: EPSRC

Diffusion MRI is an imaging technique that allows us to probe the microstructure of materials, such as the white-matter of the brain. The standard technique for estimating the orientations of the white-matter in each voxel is diffusion tensor imaging (DTI). However, DTI can only model a single fibre population in each voxel and fails for complex microstructure such as crossings. This has lead to the development of a new family of techniques, including Persistent Angular Structure (PAS) MRI and QBall, which can resolve fibre crossings and also model more complex microstructure to some extent. It is currently unclear how to exploit all the information recovered by these multiple-fibre methods. This project has several goals: how to use the shapes of the reconstructions – as opposed to just the fibre-orientation estimates – to improve predictions of microstructure and to optimise and compare multiple-fibre reconstruction algorithms to each other using a single framework.Tractography uses the fibre-orientation estimates from the reconstruction algorithms to predict connectivity between different regions of the brain. We have shown how to use the peak shapes of multiple-fibre reconstructions to predict anisotropy in the uncertainty of the fibre-orientation estimates. We use these uncertainty estimates to improve tractography through fanning regions, such as the cortico-spinal tract. More recently, we have presented an initial study into using the actual distribution of white-matter fibre orientations directly, which may differ significantly from the uncertainty (figure 1).

[1] Linear Persistent Angular Structure MRI and non-linear Spherical Deconvolution, K K Seunarine and D C Alexander, ISMRM 2006

[2] Exploiting peak anisotropy for tracking through complex structures, K K Seunarine, P A Cook, M G Hall, K V Embleton, G J M Parker and D C Alexander, IEEE ICCV Workshop on MMBIA, 2007

[3] Exploiting the fibre-orientation distribution for probabilistic tractography, K K Seunarine, S Nedjati-Gilani, M G Hall, P A Cook, and D C Alexander, ISMRM 2008

Figure 1 – Connectivity maps of the descending motor pathways using (top) the uncertainty of the fibre-orientation estimates and (bottom) the actual distribution of fibres. Light areas indicate high connectivity; dark areas low connectivity. DTI was used to estimate the fibre-orientation in each voxel.

Supervised Methods for Perfect Segmentation in Medical Images

PhD Student: Tony ShepherdSupervisors: Daniel Alexander (UCL)Funding: EPSRC

This project poses the problem of perfect segmentation, which must exactly delineate a region of interest (ROI) with exact repeatability. While fully automatic segmentation can be repeatable, accuracy is unlikely in images that suffer boundary ambiguity. This is especially problematic in ROIs such as lesions due to weak intensity gradients, unpredictable shape, the presence and similarity of texture between tissues and overlap of intensity histograms. In practice user interaction is necessary and/or desired by clinical raters, but this in turn compromises repeatability.We assert that perfect segmentation must (i) provide as complete control as possible to the user, (ii) minimise user involvement and (iii) locate ambiguous boundaries with exact repeatability. We aim to satisfy these requirements with machine learning and efficient on-line supervision. We have developed tools that combine supervised texture classification with interactive contour models [1]. The classifiers use Support Vector Machines to extract texture features. The contouring tool uses interactive, probabilistic boundary tracking.

Lesion contouring with three interactions (arrows), which (a) initialise (with straight line x0:1 and angle q0), (b) steer and (c) both steer and close the contour.

Experiments show that the classifiers are effective in disambiguating ROI boundaries and the contouring framework leads to reduced variability in segmenting larger ROIs. Experiments also highlight the challenge of balancing maximal user control (i) with minimal user involvement (ii).To improve on this balance we are extending the contouring tools to use machine learning for shape as well as texture. The shape models, based on nonlinear time series analysis, aim to characterise the global properties of otherwise unpredictable shapes without landmark points. The techniques can generate proposal shapes in a probabilistic segmentation algorithm or be used to constrain other deformable contour models. The approach also offers efficient modes of interaction.

[1] T. Shepherd and D. C. Alexander. “Supervised Methods for Perfect Segmentation in Medical Images.” In Proceedings: IEEE International Conference on Image Processing, (in print) 2008.

Integrated Technologies for in vivo Molecular Imaging

Research Associate: Vadim Y SolovievPrincipal Investigator: Simon R ArridgeAcademic Collaborators: Paul MW French, Department of Photonics, Imperial College LondonFunding: EU FP6

My recent research interests lie in the field of optical tomography, which has good potential in providing information on the cellular and tissue/organ levels. This complements existing and well adopted modalities such as computed tomography, magnetic resonance imaging and positron emission tomography. I focus mainly on developing mathematical apparatus and 3D computational techniques applicable across different types of optical imaging: near infrared tomographic imaging, bioluminescence, phosphorescence and fluorescence lifetime imaging. Currently I am participating in an EU Framework VI project, Integrated Technologies for in vivo Molecular Imaging LSHG-CT-2003-503259. In collaborative experiments between Imperial and UCL, we have demonstrated tomographic reconstruction of fluorescence lifetime distributions of Rhodamine 6G and live cells expressing EGFP embedded in highly scattering media [1-2]. Our work, which included a novel hybrid approach to data reconstruction using Fourier domain reconstruction of time-gated imaging data, was also able to take background autofluorescence into account. Recently we published a new approach based on the application of time adjoint technique (time reversal) [3]. The time reversal approach allows localization of fluorescent inclusions inside scattering media together with reconstruction of unknown optical parameters. Currently, we are developing new ideas, which will be applied for small animal imaging. Theory, image reconstruction algorithms as well as computer implementation are being addressed in our group.

[1] Soloviev VY, Tahir KB, McGinty J, Elson DS, Neil MAA, French PMW, and Arridge SR. Fluorescence lifetime imaging by using time gated data acquisition. Applied Optics 46, 7384-7391 (2007).

[2] Soloviev VY, McGinty J, Tahir KB, Neil MAA, Sardini A, Hajnal JV, Arridge SR, French PMW. Fluorescence lifetime tomography of live cells expressing enhanced green fluorescent protein embedded in a scattering medium exhibiting background autofluorescence. Optics Letters 32, 2034-2036 (2007).

[3] Soloviev VY, D’Andrea C, Brambilla M, Valentini G, Schulz RB, Cubeddu R, and Arridge SR. Adjoint time domain method for fluorescent imaging in turbid media. Applied Optics 47, 2303-2311 (2008).

Fig. Left: Phantom. The phantom’s three cavities were filled respectively with a suspension, in the culture medium, of NIH-3T3 mouse fibroblast (non fluorescent) control cells, with a suspension of the derivative clone over-expressing EGFP (fluorescent target) – middle well- and with the non-fluorescent medium alone. Right top: reconstructed lifetime at 100MHz by using the method of successful approximations. Right bottom: reconstructed quantum yield.

Modelling Breast Compressions and their Variability

Research Associate: Christine TannerPrincipal Investigator; David Hawkes Academic Collaborators: John Hipwell (UCL)Industrial Collaborators: Carestream LtdFunding: EPSRC and Technology Strategy Board

Objective:The objective of my research is to simulate breast deformations as they would occur during X-ray mammography and to create models which capture the statistics of the resulting deformations for a population. Such models could prove useful for guiding the development of algorithms to register serial X-ray mammograms [1].

Key Findings:The very large compressions (~50%) occurring during X-ray mammography require finite strain formulations to avoid involuntary volume shrinkage (~20%). However fine-meshed finite element models, employing finite strain formulations, are often unable to simulate very large deformations due to the large distortions of individual elements. Rezoning, where the deformed mesh is improved and simulation results are remapped onto the new mesh, has been proposed as a remedy and we developed a 3D rezoning method for tetrahedral elements [2]. Using this method, we have simulated plausible breast compressions for a population of 20 patients via finite element models created from segmented 3D MR breast images. Tissue properties and boundary conditions were varied according to reported values. We then created statistical deformation models of breast compressions from this population, by mapping the associated displacement fields into a common space and using principle component analysis [3]. Leave-one-patient-out tests showed that these models can reduce the mean error of unseen deformations on average by 87% when using the first 16 modes of variation. The models were able to capture the simulated deformations due to an average compression of 54% with a mean accuracy of 2.5 mm.

[1] J. H. Hipwell, C. Tanner, W. R. Crum, J. A. Schnabel, D. J. Hawkes. A new validation method for validation of x-ray mammogram registration algorithms using a projection model of breast x-ray compression, IEEE Transactions on Medical Imaging, 26(7): 1190-1200, 2007

[2] C. Tanner, T. J. Carter, D. J. Hawkes. 3D Rezoning for Finite Element Modelling of Large Breast Deformations, In Proc. European Modelling Symposium, London, UK, pp. 51-53, 2006.

[3] C. Tanner, J. H. Hipwell, D. J. Hawkes. Statistical Deformation Models of Breast Compressions from Biomechanical Simulations, to appear in Proc. Int. Workshop on Digital Mammography, 2008

Mean 1st mode

Statistical deformation model of compressed-to-undeformed breast

Computational biomechanics for medicine

Senior Research Associate: Zeike TaylorPrincipal Investigator: David Atkinson Academic Collaborators: Sebastien Ourselin (UCL), Marc Modat (UCL)Funding: EPSRC

Computational biomechanics has proven to be a useful tool in many areas of medical image computing. The nonlinear equations of continuum mechanics provide a powerful basis for modelling soft tissue deformations in interactive surgical simulators, and for constraining transformations during medical image registration. Our objectives are to develop numerical methods for rigorous and efficient modelling of soft tissue deformations, and to incorporate these in biomechanically-driven registration algorithms. We are particularly concerned with methods for intraoperative registration and registration of large scale data sets, for which computation time constraints emerge.To this end we have developed a high speed GPU-based finite element solver including an efficient procedure for simulation of anisotropic viscoelastic tissues. We used an explicit dynamic algorithm which maps well to parallel hardware. Our implementation affords computation speed improvements of around 56 times compared with serial execution, and allows real-time simulation of models with up to 55,000 degrees of freedom. Importantly our methods are kinematically and constitutively nonlinear, and are therefore valid for simulation of soft tissues under realistic finite deformations. Current work is focussed on use of the solver in a registration scheme for longitudinal MRI studies of Alzheimer’s patients.

[1] Taylor, ZA et al. Modelling anisotropic viscoelasticity for real-time soft tissue simulation, Proceedings of MICCAI 2008, New York, USA, to appear.

[2] Taylor, ZA, et al. High-speed nonlinear finite element analysis for surgical simulation using graphics processing units. IEEE Trans. Med. Imaging 2008; 27: 650-663.

[3] Taylor, ZA, et al. Real-time nonlinear FEA for surgical simulation using graphics processing units, Proceedings of MICCAI 2007, Brisbane, Australia, 701-708.

Computer aided diagnosis of neurodegenerative diseases using single and multi-source imaging data

PhD student: Benjamin ThomasSupervisors: Brian Hutton (UCL), Sebastien Ourselin (UCL), Lennart Thurfjell (3GE Healthcare) Funding: EPSRC and GE Healthcare

Dementia is one of the leading causes of death amongst elderly people. Definitive diagnosis of Alzheimer’s disease, the most common form of dementia, can only be made at autopsy. This work focuses on the use of new radiolabelled ligands, complemented by functional and anatomical imaging data. The use of multi-modality imaging offers insights into disease progression and the possibility of early detection of dementia. The desired output of this project is a computer-aided classification system for patient scans that can discriminate between different dementias as well as the effects of normal aging.Alzheimer’s disease is characterised by complex patterns of cerebral atrophy. Volume losses caused by atrophy can induce partial volume effects (PVEs), degrading the results of quantitative imaging. Partial volume correction (PVC) techniques are currently being investigated. Many PVC methods for functional imaging modalities rely on the availability of anatomical patient data. The present investigation is focused on the development of procedures that correct for PVEs using only the functional data. The work, “The use of priors in deconvolution-based partial volume correction”, was presented as a poster at the annual meeting of the British Nuclear Medicine Society 2008.

Before (left) and after (right) partial-volume correction

Image Guidance for Laparoscopic Urology Surgery

PhD Student: Steve ThompsonSupervisors: David Hawkes (UCL), Graeme Penney (KCL), Prokar Dasgupta (UCL)Funding: EPSRC

The use of laparoscopic procedures in urology is well established. The more recent introduction of telemanipulator based systems with 3D vision systems, eg the daVinci, presents an excellent opportunity to integrate image guided surgery methodologies into the surgical procedure, allowing information gathered preoperatively to be accurately aligned to the patient and displayed, for example, as an overlay on the surgeon’s monitor. Rather than requiring fiducial markers the proposed system will use the pelvic bone as a reference. This can be located in theatre using a tracked ultrasound probe. The figure below shows a system schematic.

Development and Results to DateA statistical shape model of the pelvic bone to enable segmentation of the pelvis from MRI has been developed. This has been used to automatically segment the pelvic bone from MRI with a mean surface registration error of 1.74 mm. This is used to create a patient specific model which can be aligned to the patient in theatre. Development is now focused on a robust algorithm for aligning the model to the patient accounting for expected model errors by transforming ultrasound and model data to bone surface probability maps. Development is being performed on a custom built pelvic anatomy phantom.

[1] S. Thompson , G. Penney , D. Buie , P. Dasgupta , D. Hawkes “Use of a CT statistical deformation model for multi-modal pelvic bone segmentation.” Proceedings of the SPIEMedical Imaging 2008:Image Processing.

[2] S. Thompson, G. Penney, P. Dasgupta, D. Hawkes “Image Guidance in Robot AssistedRadical Prostatectomy” Presented at EAU 23rd Annual Congress, March 2008

Patient specific model of pelvic bone aligned with patient data during surgical intervention

Quantification of blood flow from rotational angiography

Student: Irina WaechterSupervisors: David Hawkes (UCL), Dean Barratt (UCL) Academic Collaborators: Nick Ovenden (Mathematics department, UCL) Daniel Ruefenacht (University Hospital of Geneva)Industrial Collaborators: Philips Research Europe, Aachen, Germany Philips Medical Systems, Best, The NetherlandsPhilips Research Europe, Aachen, Germany

For assessment of cerebrovascular diseases, it is beneficial to obtain three-dimensional (3D) information on vessel morphology and haemodynamics. Rotational angiography is routinely used to determine the 3D geometry. I proposed a method to exploit the same acquisition to determine the blood flow waveform and the mean volumetric flow rate in the large cerebral arteries.The method uses a model of contrast agent dispersion to determine the flow parameters from the spatial and temporal progression of the contrast agent concentration, represented by a flow map. Furthermore, it overcomes artifacts due to the rotation (overlapping vessels and foreshortened vessels at some projection angles) of the C-arm using a reliability map.The method was validated on images from different phantom experiments. I analyzed different properties of the flow quantification method, including the influence of noise and the influence of the length of the analyzed blood vessel.

In most cases, the relative error was between 5% and 10% for the volumetric mean flow rate and between 10% and 15% for the blood flow waveform. The manual interaction took at most one minute and the computational time for the flow quantification was between 4 and 20 minutes on a PC. From this, I conclude that the method has the potential to give quantitative estimates of blood flow parameters during cerebrovascular interventions.

[1] I. Waechter, J. Bredno, J. Weese, D. C. Barratt, and D. J. Hawkes, “Quantification of blood flow from rotational angiography,” in MICCAI, 634-641, 2007.

[2] I. Waechter, J. Bredno, R. Hermans, J. Weese, D. C. Barratt, and D. J. Hawkes, “Evaluation of model-based blood flow quantification from rotational angiography,” in SPIE Medical Imaging, 2008.

Free-breathing liver MRI reconstruction

Research Associate: Mark WhitePrincipal Investigator: Dave Hawkes (UCL) Co-Investigator: David Atkinson (UCL)Academic Collaborators: Andrew Melbourne, Freddy Odille (UCL), Martin Leach, David Collins, Dow-Mu Koh, Matthew Orton, Keiko Miyazaki (Institute of Cancer Research and Royal Marsden NHS Foundation Trust); Catherine Coolens, Maria Hawkins (Royal Marsden NHS Foundation Trust)Funding: EPSRC

The liver lies immediately below the diaphragm, and moves with breathing. This makes practical hepatic MRI challenging due to motion artefacts (blurring andghosting) in the liver. For both dynamic studies where a contrast agent is injected and its uptake monitored over a period of several minutes, and some morphological studies, neither breath-holding nor respiratory gating approaches are satisfactory, as both place limits on the period of continuous acquisition. This project investigates approaches to continuous MRI of the liver during free breathing, by modelling liver motion and correcting for it during image reconstruction.The full process consists of three phases. First, a low-resolution training series is registered and used to build a parameterized non-rigid model of breathing motion.Second, the novel Image Deformation Recovery from Overlapping Partial Samples (iDROPS) process then finds a time-series of parameter values from a higher-resolution imaging acquisition by comparing the parts of k-space which overlap with the training data; this leads to an estimated deformation for each fragment of acquired k-space. Thirdly, artefacts in free-breathing liver MRI may be corrected by building a general matrix model of deformed acquisition and inverting it using a conjugate-gradient approach to find the ‘true’ image.The deformation fields estimated by model-based iDROPS are accurate to 1.5mm in the liver (average of 10 volunteer studies, using synthetic reduced acquisition). The full process has been demonstrated to reduce motion artefacts in continuously-acquired free-breathing liver data. One such reconstruction is below, showing a single sagittal slice of the liver: a reference (one k-space average, acquired during breath-hold) is shown on the left, followed by uncorrected and corrected images (each composed of twenty k-space averages, acquired during free breathing).

Reconstruction of Digital Breast Tomosynthesis Data

PhD Student: Guang YangSupervisors: Simon Arridge (UCL)Academic Collaborators: John HipwellIndustrial Collaborator: Dexela LtdFunding: Technology Strategy Board

Digital Tomosynthesis (DTS) is a 3-dimensional medical imaging technique which can reconstruct an arbitrary cross-section of an imaged object using 3-dimensional back-projection or iterative techniques. Compared with widely used conventional Computed Tomography (CT), DTS offers advantages such as low radiation dosage and simultaneous multi-plane reconstruction. DTS is particularly advantageous when X-ray dosage, narrow angle of view and fast imaging are of primary concern.An important application of DTS is Digital Breast Tomosynthesis (DBT) which is an extension of mammography. Compared with standard mammography techniques, DBT makes it possible to distinguish cancer from its overlying breast tissue. This is of particular relevance for the dense breasts of young women which can inhibit detection of cancer using conventional mammography. In the same way that it is difficult to see a bird in a forest from the edge of the forest, detecting cancer in a conventional 2-dimensional mammogram is a challenging task. 3-dimensional DBT, however, enables us to step through the forest, reducing the confounding effect of superimposed tissue and so increasing the sensitivity and specificity of cancer detection.At the initial stage of this research, the aim is to investigate existing algorithms for DBT reconstruction and subsequently develop new methods to combine DBT reconstruction and registration to improve both the reconstruction and alignment of temporal data sets.

An example of the previous research results of the reconstruction of DBT by using FBP(Filtered Back-projection) and GFB (Gaussian Frequency Blending)

Parameter and Structure Identification in Optical Tomography

Research Associate: Athanasios ZacharopoulosPrincipal Investigator: Simon Arridge Academic Collaborators: Martin Schweiger (UCL), Abdel Douiri (UCL)Funding: EPSRC, FP6 EU

My main research interest is mathematical methods for the reconstruction of images from data collected on the surface of scattering mediums. The chosen modality that is used as a test-bed for the reconstruction algorithms is Diffusion Optical Tomography, utilising data collected from the Medical Physics Lab in UCL as well as several labs around the world. More specific in the last years my work was on a Boundary Elements - Shape Based reconstruction technique. We used Spherical Harmonics to describe parametrically the surface of the regions under interest in the reconstructions. We presented simulated results and now we proceed with the application of the method in experimental measurements that has the advantage of a simple surface description for the needs of the numerical method and a small number of unknowns-shape parameters that improve the ill-posedness of the non-linear problem.A different application that we were involved in, was using multi-wavelength fluorescence data acquired in the Lund Laser Lab in Sweden. In this case the requirement was the reconstruction of the concentration of injected fluorochrome in tissue. In this project a reconstruction using finite elements was utilised, and a novel method that reduced the dimensionality of the computations of the problem and therefore could take advantage of the large number of measurements that are typical for non-contact acquisition method that have evolved in the last years.My current research involves the use of the experience already gained in the above fields for the inclusion of structural information; acquired using an alternative structural modality, in the reconstructions for a diffusion tomography. Apart, from the already mentioned computational tools a statistical approach to the inverse problem will be used.

Figure: Reconstruction result from fruorochrome concentration in a mouse.

[1] J. Sikora, A. Zacharopoulos, A. Douiri, M. Schweiger, L. Horesh, S. R Arridge and J. Ripoll, “Diffuse photon propagation in multilayered geometries”, Phys. Med. Bio.l 51 497-516 (2006).

[2] D. Zacharopoulos, S. Arridge, O. Dorn, V.Kollehmainen and J. Sikora, Three-dimensional reconstruction of shape and piecewise constant region values for optical tomography using spherical harmonic parametrization and a boundary element method” Inverse Problems 22 1509-1532 (2006)

An atlas-based segmentation propagation framework using locally affine registration – Application to automatic whole heart segmentation

PhD Student: Xiahai ZhuangSupervisors: Derek Hill (UCL), David Hawkes (UCL), Sebastien Ourselin (UCL)Academic Collaborators: Reza Razavi (KCL), Kawal Rhode (KCL), Simon Arridge (UCL)Funding: EPSRC

We present a novel registration algorithm for locally affine registrations. This method preserves the anatomical and intensity class relationships between the local regions. A regularisation procedure is used to maintain a global diffeomorphic transformation. Combined with a novel method for accurately inverting the final deformation field, we included our technique within an atlas-based segmentation propagation framework. We applied our method to automatically segment the whole heart from cardiac magnetic resonance images from a cohort of 18 volunteers (acquisition resolution 2 2 2 mm). The results show that the proposed method provides a robust initialisation for the atlas-based segmentation propagation framework refined with a fluid registration. We validated our approach against other registration strategies, and demonstrated that we improved the accuracy of the whole heart segmentations (1.8 0.42mm). We presented our results into our latest MICCAI publication [1].

[1] Z. Zhuang, R. Razavi, D. Hill, D. Hawkes, S. Ourselin. An atlas-based segmentation propagation framework using locally affine registration - Application to automatic whole heart segmentation. Oral presentation in 11th MICCAI (2008).

2008 Refereed Journal Papers

• Barratt, DC., Chan, CSK., Edwards, PJ., Penney, GP., Slomczykowksy, M., Carter, TJ., Hawkes, DJ. (2008).Instantiation and registration of statistical shape models of the femur and pelvis using 3D ultrasound imaging. Medical Imag Analysis

• Boyes,R.G.,Gunter,J.L.,Frost,C.,Janke,A.L.,Yeatman,T.,Hill,D.L.G.,Bernstein,M.A.,Thompson,P.M.,Weiner,M.W., Schuff, N., Alexander, G.E., Killiany, R.J., Decarli, C., Jack, C.R., Fox,N.C. (2008). Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils. NeuroImage 39(4), 1752-1762. ISSN: 1053-8119

• Chandler,AG., Pinder,RJ.,Netsch,T, Schnabel, JA.,Hawkes,DJ.,Hill,DLG.,RazaviR. (2008),Correction ofmisaligned slices in multi-slice cardiovascular magnetic resonance using slice-to-volume registration. J. Cardiovascular Magnetic Resonance 10:13-21

• Cheng,M.,Taylor,Z.A.,Ourselin,S.(2008).Towardsanatomicalmodellingofmultipleorgansinteractionusingrealtime GPU based nonlinear elasticity. Stud Health Technol Inform NA

• Hansen, M.S., Atkinson, D., Sorensen, T.S. (2008). Cartesian SENSE and k-t SENSE Reconstruction usingCommodity Graphics Hardware. Magnetic Resonance in Medicine 59(3), 463-468. ISSN: 0740-3194

• Hipwell, J.,Tanner,C.,Crum,WR.,Schnabel, J.,Hawkes,DJ.(2008).AnewvalidationmethodforvalidationofX-ray Mammogram Registration Algorithms using a Projection Model of Breast X-ray Compression. IEEE Tran Med Imag 26, 1190-1200

• Kloppel,S.,Draganski,B.,Golding,C.V.,Chu,C.,Nagy,Z.,Cook,P.A.,Hicks,S.L.,Kennard,C.,Alexander,D.C.,Parker, G.J., Tabrizi, S.J., Frackowiak, R.S. (2008). White matter connections reflect changes in voluntary-guided saccades in pre-symptomatic Huntington’s disease. Brain 131(1), 196-204. ISSN: 0006-8950

• McClelland,J.,Webb,S.,McQuaid,D.,Binnie,D.M.,Hawkes’DJ.(2008).Tracking“differentialorganmotion”witha “breathing” multileaf collimator: magnitude of problem assessed using 4D CT data and a motion-compensation strategy. Physics in Medicine and Biology 52(16), 4805-4826. ISSN: 0031-9155

• OrtonMR,d’ArcyJA,Walker-SamuelS,HawkesDJ,AtkinsonD,CollinsDJ,LeachMO(2008)“Computationallyefficient vascular input function models for quantitative kinetic modelling using DCE-MRI”, Phys Med Biol, 53, 1225-1339

• Orton,MR.,Collins,DJ.,Walker-Samuel,S.,d’Arcy,JA.,Hawkes,DJ.,Atkinson,D.,Leach,MO.(2008).Bayesianestimation of pharmacokinetic parameters for DCE-MRI with a robust treatment of enhancement onset time. Physics in Medicine and Biology 52(9), 2393-2408. ISSN: 0031-9155

• Powell,H.W.R.,Parker,G.J.M.,Alexander,D.C.,Symms,M.R.,Boulby,P.A.,Barker,G.J.,Thompson,P.J.,Koepp,M.J., Duncan, J.S. (2008). Imaging language pathways predicts postoperative naming deficits. Journal of Neurology, Neurosurgery and Psychiatry 79(3), 327-330. ISSN: 0022-3050

• Silberberg, Y.R., Pelling, A.E., Yakubov,G.E., Crum,W.R.,Hawkes,D.J.,Horton.M,A, (2008).Mitochondrialdisplacements in response to nanomechanical forces. Journal of Molecular Recognition 21(1), 30-36. ISSN: 0952-3499

• Taylor,Z.A.,Cheng,M.,Ourselin,S. (2008).High-speednonlinearfiniteelementanalysis forsurgical simulationusing graphics processing units. IEEE Trans Med Imaging 27(5), 650-663. ISSN: 0018-9499

2008 Conference Publications• Acosta,O., Frimmel,H., Salvado,O.,Ourselin, S. (2008).Pyramidal Flux in an Anisotropic Diffusion Scheme for

Enhancing Structures in 3D Images, Proceedings AUings of SPIE: Image Processing• CarterT,Beechy-NewmanN,TannerC,HawkesDJ(2008),NavigatedBreastSurgery:MethodandInitialClinical

Experience, Proc MICCAI 2008• Comas,O.,Taylor,Z.A.,Allard,J.,Ourselin,S.,Salvado,O.,Cotin,S.,Passenger,J.(2008).EfficientnonlinearFEM

for soft tissue modelling and its GPU implementation within the open source framework SOFA. International Symposium on Computational Models for Biomedical Simulation, London

• Enfield,L.C.,Gibson,A.P.,Everdell,N.L.,Hebden,J.C.,Arridge,S.R.,Sharma,A.,Sainsbury,R.,Douek,M.,Keshtger,M. (2008). Sensitivity and specificity of 3D optical mammography. OSA Biomedical Optics Topical Meeting

• Gibson,A.P.,Enfield,L.C.,Schweiger,M.,Arridge,S.R.,Douek,M.,Hebden,J.C.(2008).3DOpticalmammographyof the uncompressed breast. OSA Biomedical Optics Topical Meeting

• Hansen,M.S., Atkinson,D., Sorensen, T.S. (2008). Interactive Adjustment of Regularization in SENSE and k-tSENSE using Commodity Graphics Hardware. ISMRM, , 1491

• M.X.Hu,M.X.,Penney,GP.,Rueckert,D.,Edwards,P.J.,Figl,M.,Pratt,P.,Hawkes,D.J.Anovelmethodforheartmotion analysis based on geometry estimation, 11th International Conference on Medical Image Computing and Computer Assisted Intervention 2008

• Hu,Y.,Morgan,D.,Ahmed,H.,Pendsé,D.,Sahu,M.,Allen,C.,Emberton,M.,Hawkes,D.,Barratt,D.AStatisticalMotion Model based on Biomechanical Simulations for Data Fusion during Image-guided Prostate Interventions. Proc. MICCAI 2008

• Melbourne,A.,Atkinson,D.,etal. (2008). InfluenceofOrganMotionandContrastEnhancementonSuccessfulRegistration. MICCAI,

• Melbourne, A., Hawkes, D.J., Atkinson, D. (2008). Non-Rigid Registration of DiffusionWeighted MRI usingProgressive Principal Component Registration. ISMRM, 3097

• Miyazaki,K.,Collins,D.J.,Koh,D-M.,Atkinson,D.,Hawkes,D.J.,Leach,M.O.(2008).Evaluationofquantitativedynamic contrast enhanced pharmacokinetic parameters with and without postprocessing. ISMRM, 3734

• Orton,M.R.,Collins,D.J.,Hawkes,D.J.,Atkinson,D.,Leach,M.O.(2008).EffectofImageAcquisitionProtocolonVascular Parameter Estimates from DCE-MRI Liver Data. ISMRM, 1894

• Orton,M.R.,d’Arcy, J.A.,Collins,D.J.,Atkinson,D.,Hawkes,D.J.,Leach,M.O.(2008).PortalDelayEstimationfrom DCE-MRI Liver Tissue Data: Feasibility and Effect on Vascular Parameter Estimates. ISMRM, 1709

• Padormo,F.,Nunes,R.G.,Atkinson,D.,Batchelor,P.G.,Hajnal,J.V.,Larkman,D.J.(2008).AnApproachtoCoilCalibration Based on Prior Training Data. ISMRM, 1277

• Porter,D.A.,Atkinson,D.,Scott,R.,Clark,C.A.(2008).High-ResolutionDiffusionTensorImagingRevealsSub-Structure whithin Human Hippocampus in vivo. ISMRM, 3362

• Sørensen, T.S., Atkinson, D., Boubertakh, R., Schaeffte, T., Hansen, M.S. (2008). Rapid non-Cartesian ParallelImaging Reconstruction on Commodity Graphics Hardware. ISMRM, 1490

• Taylor,Z.A.,Comas,O.,Cheng,M.,Passenger,J.,Hawkes,D.J.,Atkinson,D.,Ourselin,S.(2008).Modellinganisotropicviscoelasticity for real-time soft tissue simulation. Medical Image Computing and Computer-Assisted Intervention, New York

• WaechterI,BrednoJ,WeeseJ,HermansR,BarrattDC,andHawkesD(2008),Evaluationofmodelbasedbloodflowquantification from Rotational Angiography 5th IEEE International Symposium on Biomedical Imaging

• Waechter,I.,Barratt,D.C.Hawkes,D.J.,Bredno,J.,Weese,J.(2008).Quantifyingbloodflowdivisionatbifurcationsfrom rotational angiography. 5th IEEE International Symposium on Biomedical Imaging

• White,M.J.,Atkinson,D.,Charles-Edwards,L.,Coolens,C.,Hawkins,M.,Miyazaki,K.,Collins,D.,Leach,M.O.,Hawkes, D.J. (2008). Image Deformation Recovery using Overlapping Partial Samples (iDROPS): model-based respiratory artefact correction in freebreathing. ISMRM, 3117

• Zhuang,X.,Ourselin,S.,Rhode,K.,Hawkes,D.(2008),Anatlas-basedsegmentationpropagationframeworkusinglocally affine registration – Application to automatic whole heart segmentation, Proc MICCAI 2008

• Zhuang,X.,Hawkes,D.J.,Crum,W.R.,Boubertakh,R.,Uribe,S.,Atkinson,D.,Batchelor,P.,Schaeffter,T.,Razavi,R., Hill, D.L.G. (2008). Robust Registration between Cardiac MRI Images and Atlas for Segmentation Propagation. Proceedings of SPIE Vol. 6914 Medical Imaging 2008: Image Processing, 6914, 07

• Zuluaga,M.,Acosta,O.,Bourgeat,P.,Silvado,O.,Hernandez,M,.Ourselin,S(2008).CorticalThicknessMeasurementfrom Magnetic Resonance Images Using Partial Volume Estimation, Proceedings of SPIE: Image Processing

2007 Refereed Journal Papers• DouiriA.,Schweiger,M.,RileyJ.andArridgeS.(2007).Anisotropicdiffusionregularizationmethodsfordiffuseoptical

tomography using edge prior information. Measurement Science & Technology 18(1), 87-95. ISSN: 0957-0233• Cox,.T.,Kara,S.,Arridge,S.R.,Beard,P.C.(2007).k-spacepropagationmodelsforacousticallyheterogeneousmedia:

application to biomedical photoacoustics. Journal of the Acoustical Society of America 121(6), 3453-3464. ISSN: 0001-4966

• Enfield,L.C., Gibson,A.P., Everdell,N.L., Delpy,D.T., Schweiger,M., Arridge,S.R., Richardson,C., Keshtgar,M.,Douek,M., Hebden,J.C. (2007). Three-dimensional time-resolved optical mammography of the uncompressed breast. Applied Optics 46(17), 3628-3638. ISSN: 0003-6935

• Hipwell,J.H.,Tanner,C,Crum,W.R.,Schnabel,J.A.,Hawkes,D.J.(2007).Anewvalidationmethodforvalidationofx-ray mammogram registration algorithms using a projection model of breast x-ray compression. IEEE Transactions on Medical Imaging 26(7), 1190-1200. ISSN: 0278-0062

• McClellandJ.,WebbS.,McQuaidD.,BinnieD.MandHawkesD.J.(2007)“Tracking“differentialorganmotion”witha “breathing” multileaf collimator: magnitude of problem assessed using 4D CT data and a motion-compensation strategy” Physics in Medicine and Biology. 52, 4805-4826

• Melbourne, A., Atkinson,D.,White,M.J., Collins,M., Leach,M.,Hawkes,D. (2007). Registration of dynamiccontrast-enhanced MRI using a progressive principal component registration (PPCR) Physics in Medicine and Biology 52(17), 5147-5156. ISSN: 0031-9155

• Nagy,Z.,Weiskopf,N.,Alexander,D.C.,Deichmann,R.(2007).Amethodforimprovingtheperformanceofgradientsystems for diffusion-weighted MRI. Magnetic Resonance in Medicine 58(4), 763-768. ISSN: 0740-3194

• Orton,M.R,Collins,D.J.,Walker-Samuel,S.,d’Arcy,J.A.,Hawkes,D.J.,Atkinson,D.,Leach,M.O.(2007).Bayesianestimation of pharmacokinetic parametersfor DCE-MRI with a robust treatment of enhancement onset time. Physics in Medicine and Biology 52(9), 2379-2391. ISSN: 0031-9155

• Penney,GP.,Edwards,PJ.,Hipwell,JH.,Slomczykowski,M.,Revie,I.,Hawkes,DJ.,(2007)“Postoperativecalculationof acetabular cup position using 2-D-3-D registration”, IEEE Trans Biomed Eng, 54, 1342-1348

• Powell,H.W.,Parker,G.J.M.,Alexander,D.C.,Symms,M.R.,Boulby,P.A.,Wheeler-Kingshott,C.A.M.,Barker,G.J.,Koepp, M.J., Duncan, J.S. (2007). Abnormalities of language networks in temporal lobe epilepsy. NeuroImage 36(1), 209-221. ISSN: 1053-8119

• Tanner,C.,Schnabel,J.A.,Degenhard,A.,Hall-Craggs,M.A.,Usiskin,S.I.,Leach,M.O.,Hose,D.R.,Hill,D.L.G.,Hawkes, D.J.. (2007). Quantitative evaluation of free-form deformation registration for dynamic contrast-enhanced MR mammography. Medical Physics 34(4), 1221-1233. ISSN: 0094-2405

2007 Conference Proceedings• Alexander,D.(2007),Axon radius measurements in vivo from diffusion MRI: a feasibility study. Proceedings of MMBIA

2007: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis. In conjunction with ICCV 2007: Eleventh IEEE International Conference on Computer Vision. Rio de Janeiro, Brazil, October 14-20, 2007.

• Atkinson,D.,Batchelor, P.G.,Clark,C. (2007).TrackRibbons -Visualising Structural Information inDiffusionTensor Axial Asymmetry. Proc ISMRM, 79

• Batchelor,P.Atkinson,D.Schaeffter,T.,Razavi,R.(2007).AfastPrincipalDirectionImagingmethodfromDiffusionWeighted Images. Proceedings ISMRM 3516

• Cahill,N.,Noble,J.,Hawkes,D.(2007).FourierMethodsforNonparametricImageRegistration,Proc CVPR 2007. • Dindoyal,I.,Lambrou,T.,Deng,J.,Todd-Pokropek,A.(2007).Levelsetsnakealgorithmsonthefetalheart.IEEE

International Symposium on Biomedical Imaging, Arlington, VA, USA:IEEE, 864-867• Dindoyal, I., Lambrou, T.,Deng, J., Todd-Pokropek, A. (2007). Automatic segmentation of low resolution fetal

cardiac data using snakes with shape priors. IEEE ISPA, 538-543• Hawkes,D.J.,Penney,G.P.,Atkinson,D.,Barratt,D.,Blackall, J.,Carter,T.,Crum,B.,McClelland.J.,Tanner,C.,

Tarte, S., White, M.J. (2007). Motion and biomechanical models for image guided interventions. Proc IEEE In Symp of Biomedical Imag, 992-995

• Hu,M.,Penney,GP.,Edwards,PJ.,Figl,M.,Hawkes,DJ.3DReconstructionofInternalOrganSurfacesforMinimalInvasive Surgery, Proc MICCAI 2007

• Mang,A.,Camara,O.,Brasil-Caseiras,G.,Crum,W.,Schnabel,J.,Buzug,T.,Rees,J.,Thornton,J.,Jaeger,R.,Hawkes,DJ. Registration of RBCV and ADC Maps with structural and physiological MR images in glioma patients: study and validation, Proc IEEE International Symposium of Biomedical Imaging: from Nano to Macro

• McClelland et al.Non-rigid registrationbased respiratorymotionmodelsof the lungusing twoparameters,Proc AAPM 2007

• Melbourne, A., Atkinson, D.,White,M.J., Hawkes, D.J., Collins, D., Leach,M. (2007). Contrast-enhancementsimulation using principal components for registration of Dynamic Contrast-Enhanced MRI. ISMRM, 522

• Melbourne, A., Atkinson, D., White, M.J., Hawkes, D.J., Collins, D., Leach, M. (2007). Using Registration toQuantify the Consistency of Whole Liver Position during Patient Breath-hold in Dynamic Contrast-Enhanced MRI. Proc ISMRM, 3709

• Miquel,M.,Blackall,J.,Uribe,S.,Leussler,C.,Schaeffer,T.,Hawkes,D.Dynamic3DlungMRIusinga32channelcoil array for the construction of respiratory motion models, Proc ISMRM, Berlin, p. 586, 2007

• Miyazaki, K., d’Arcy, J.A., Atkinson,D.,Hawkes,D., Koh,D-M., Leach,M.O., Collins,D.J. (2007).CorrectingArtifacts in High Temporal- and Spatial-Resolution Dynamic Abdominal Studies Using UNFOLD: A Potential Tool for Improving Perfusion Quantification of DCE-MR Investigations. ISMRM, 3421

• Morgan,D.,UddinAhmed,H.,Emberton,M.,Hawkes,D.,Barratt,D.(2007).RegistrationofpreoperativeMRtointraoperative ultrasound images for guiding minimally invasive prostate interventions. Medical Image Understanding and Analysis (MIUA) Proc. MIUA series. , 181-185

• Nunes,R.G.,Hajnal,J.V.,Atkinson,D.,Larkman,D.J.(2007).CanRelativeCoil-SubjectMotionDuringAcquisitionImprove Parallel Imaging? ISMRM, 3340

• Waechter, I.,Bredno, J.,Barratt,D.,Weese, J.,Hawkes,D. (2007).QuantificationofBloodFlowfromRotationalAngiography. Medical Image Computing and Computer-aided Interventions, MICCAI series.

• White,M.J.,Atkinson,D.,Batchelor,P.G.,Crum,W.R.,Uribe,S.,Schaeffter,T.,Collins,D.J.,Leach,M.O.,Hawkes,D.J. (2007). Reducing Artefacts by Inverting the Effects of Non-Rigid Motion During a Free-Breathing Liver Scan. ISMRM, 3430

2006 Refereed Journal Papers• Arridge,S.R.,Kaipio, J.P.,Kolehmainen,V.,Schweiger,M.,Somersalo,E.,Tarvainen,T.,Vauhkonen,M., (2006).

Approximation errors and model reduction with an application in optical diffusion tomography. Inverse Problems 22, 175-195. ISSN: 0266-5611

• Atkinson, D. (2006). Incoherent Artefact Correction using PPI. NMR in Biomedicine 19(3), 362-367. ISSN: 0952-3480

• Austin,T.,Gibson,A.P.,Branco,G.,Yusof,R.,Arridge,S.R.,Meek,J.H.,Wyatt,J.S.,Delpy,D.T.,Hebden,J.C.(2006).Three dimensional optical imaging of blood volume and oxygenation in the neonatal brain. Neuroimage 31(4), 1426-1433. ISSN: 1053-8119

• Barratt,D.C.,Penney,G.P.,Chan,C.S.K.,Slomczykowski,M.,Carter,T.J.,Edwards,P.J.,Hawkes,D.J.(2006).Self-Calibrating 3D-Ultrasound-Based Bone Registration for Minimally Invasive Orthopedic Surgery. IEEE Transactions on Medical Imaging 25(3), 312-323. ISSN: 0278-0062

• Blackall, J.,Ahmad,S.,Miquel,M.,McClelland, J.,Landau,D.,Hawkes,D.(2006).MRI-BasedMeasurementsofRespiratory Motion Variability and Assessment of Imaging Strategies for Radiotherapy Planning. Physics in Medicine and Biology 51(17), 4147-4169. ISSN: 0031-9155

• CarterT.J.,SermesantM.,BarrattD.C.,HawkesD.J.Applicationofsofttissuemodellingtoimage-guidedsurgery.Med. Eng. Phys.(2006) 27(10):893-909

• McClelland,J.,Blackall,J.,Tarte,S.,Chandler,A.,Hughes,S.,Ahmad,S.,Landau,D.,Hawkes,D.(2006).AContinuous4D Motion Model from Multiple Respiratory Cycles for use in Lung Radiotherapy. Medical Physics 33(9), 3348-3358. ISSN: 0094-2405

• Papastavrou,Y.,Hutton,B.,Hawkes,D.(2006).Non-rigidregistrationofcombnedPET-CTjheadandneckimagesto place the information within a common anatomical framework. Eur J Nucl Med Mol Imaging 33, S138-S138

• Penney, G.P., Barratt, B.C., Chan, C.S.K., Slomczykowski, M., Carter, T.J., Edwards, P.J., Hawkes, D.J. (2006).Cadaver Validation of intensity-based ultrasound to CT registration. Medical Image Analysis 10(3), 385-395. ISSN: 1361-8415

• Tanner, C.Schnabel, J.A., Leach, M.O., Hose, D.R., Hill, D.L.G., Hawkes, D.J. (2006). Factors Influencing theAccuracy of Biomechanical Breast Models. Medical Physics 33(6), 1758-1769. ISSN: 0094-2405

2006 Conference Publications

• Archbold,P.,Slomczykowski,M.,Penney,G.,Barratt,D.,Beverland,D.(2006).Acadavericstudyinvestigatingtheplacement of the acetabular component in total hip arthroplasty by reference to the acetabular labrum and transverse acetabular ligament: defining a patient-specific, ‘functional’ safe-zone for cup placement. 6th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, Series edited by Langlotz,F., Davies,B.L.

• Atkinson,D.,Counsell,S.,Hajnal,J.,Larkman,D.J.,Batchelor,P.G.,Hill,D.L.G.(2006)

• NavigatedMulti-ShotEPIDiffusionImagingoftheSpine.Proceedings of the ISMRM 1038• Barratt,D.C.,Penney,G.P.,Slomczykowski,M.,Hawkes,D.J.(2006).Ultrasound-basedregistrationforminimally-

invasive orthopaedic surgery. Presentations by Britain’s Top Younger Scientists, Engineers and Technologists at The House of Commons

• Barratt,D.C.,Penney,G.P., Slomczykowski,M.,Hawkes,D.J. (2006).RecentDevelopments inUltrasound-basedBone Registration. Computer Assisted Orthopaedic Surgery: 6th Annual Meeting of CAOS International, Langlotz,F., Davies,B.L., Ellis,R.E. (ed.) , 22-24

• Batchelor,P.G.,Atkinson,D.,Larkman,D. J.,Hajnal, J.V. (2006).Correctionsand reconstructionvia conjugategradientmethods, comparison of matrix and functional methods. ESMRMB

• Batchelor,P.,Calamante,F.,Atkinson,D.,Hill,D.L.G.,Tournier,D.,Connelly,A.(2006).AdvancesinFiberTrackingQuantification. Proceedings of the ISMRM, 3171

• Batchelor, P.,Calamante,F .,Atkinson,D.,Hill,D.L.G.,Tournier,D.,Connelly,A. (2006).HigherOrderTensorAnalysis and Higher Order SVD for Diffusion Tensor Analysis. Proceedings of the ISMRM, 3172

• Batchelor,P.G.,Atkinson,D.,Larkman,D.J.,Hajnal, J.,Hill,D.L.G., Irrarazaval,P.,Hansen,M.(2006).ASparseMatrix Formalism for Non-Rigid Motion Correction. Proceedings of the ISMRM, 3205

• Dindoyal, I.,Lambrou,T.,Deng,J.,Todd-Pokropek,A.(2006).Fullyautomatedshapepriorappliedtothefoetalheart - preliminary results. Medical Imaging Understanding and Analysis, Manchester, UK

• Hipwell, J.,Tanner,C.,Crum,WR.,Hawkes,DJ.X-raymammographic registration: a novel validationmethod,Digital Mammography Springer LNCS 4046, 197-204, 2006

• Miyazaki,K.,Walker-Samual,S.,Knowles,B.R.,Taylor,N.J.,Padhani,A.R.,Wallace,T.,Tang,A.,White,M.,Hawkes,D., Atkinson, D., Collins, D., Leach, M.O., Koh, D.M. (2006). Parametric DCE-MRI HPI Maps - A Qualitative and Quantitative Analysis in Phase I Clinical Trial Patients with Liver Metastases. Proceedings of the ISMRM, 2006. , 763

• Tanner,C,Khazen,M.,Kessar,P.,Leach,M.O.,Hawkes,D.J.(2006).DoesRegistrationImprovethePerformanceof a Computer Aided Diagnosis System for Dynamic Contrast-Enhanced MR Mammography? IEEE International Symposium on Biomedical Imaging

• Tanner,C.,Carter,T.J.,Hawkes,D.J.(2006).3DRezoningforFiniteElementModellingofLargeBreastDeformations.European Modelling Symposium, 51-53

• Sneller,B.,Garde,E.,Atkinson,D.,Batchelor, P., Fox,N.C.,Hill,D.L.G. (2006). FLOWARTIFACTS INMRIMAGING FOR ALZHEIMER’S DETECTION AND PROGRESSION. 10th International Conference on Alzheimer’s Disease and Related Disorders

• White, M.J., Atkinson, D., Collins, D., Leach, M.O., Hawkes, D.J. (2006). Parameterized modelling of liverdeformation during free. ESMRMB

INM PHYSICS PUBLICATIONS 2008

Papers• McQuaid,S.,Hutton,B.A.,comparisonbetweenrespiratory-inducedattenuation-correctionartefactsinPET/CT

and SPECT/CT. Eur J Nucl Med Mol Imaging 2008; 35: 1117-23.• Groves,AM.,Goh,V.,RajasathnathanS,Dickson J, EndozoR,MenezesLJ, ShastryM,Ell, PJ,HuttonBF.CT

coronary angiography: Quantitative assessment of myocardial perfusion using test bolus data– initial experience European Radiology 2008 (in press)

• Hamann,M.,AldridgeM.,Dickson,J.,Endozo,R.,Lozhkin,K.,Hutton,B.,Evaluationofalow-dose/slow-rotatingSPECT-CT system. Phys Med Biol 2008; 53: 2495-2508.

• Gambhir,SS.,Berman,DS.,Ziffer,J.,Nagler,M.,Dalia.,Dickman,D.,Rousso,B.,Sandler,M.,Patton.J,,Hutton,B.,Dichterman, E., Ziv, O., Melman, H., Zilberstein, Y., Ben Haim, S. and Ben Haim, S. A. Novel High Sensitivity Rapid Acquisition Single Photon Molecular Imaging Camera. Submitted to J Nucl Med 2008.

• Barnden,J.,Hutton,B.EmissiontomogramsasinputmodelsforMonteCarlopatient-basedsimulations:propagationof input noise and resolution. Submitted to IEEE Trans Nucl Sci, 2008.

Conferences• Fiorini,C.,Gola,A.,Peloso,R.,Longon,iA.,Lechner,P.,Strüder,L.,Hutton,BF.,Erlandsson,K.,Mahmood,S.,Van

Mullekom, P., Pedretti, A., Moretti, R., Poli, GL., Lucignani, G. HICAM: development of a high-resolution Anger

Camera for nuclear medicine. Submitted to IEEE Nucl Sci Symp and Med Imaging Conf, 2008.• Erlandsson,K.,Núñez,M.andHutton,BF.ReductionofCTartifactsduetorespiratorymotioninaslowlyrotating

SPECT/CT. Submitted to IEEE Nucl Sci Symp and Med Imaging Conf, 2008.• Mahmood,S.,Erlandsson,K.andHutton,B.ImprovedReconstructedImageQualityinaSPECTSystemwithSlit-

Slat Collimation by Combination of Multiplexed and Non-Multiplexed Data. Submitted to IEEE Nucl Sci Symp and Med Imaging Conf, 2008.

• Kacperski,K.,Erlandsson,K.,Ben-Haim,S.,VanGramberg,D.,HuttonBF.IterativeDeconvolutionofSimultaneousDual Radionuclide Projections for CdZnTe Based Cardiac SPECT. Submitted to IEEE Nucl Sci Symp and Med Imaging Conf, 2008.

• McQuaid,S.,Lambrou,T.,Hutton,BF.Statisticalshapemodelingofthediaphragmforapplicationto82-RbcardiacPET-CT studies. Submitted to IEEE Nucl Sci Symp and Med Imaging Conf, 2008.

INM PHYSICS PUBLICATIONS 2007

Papers

• Beekman,F.,Hutton,BF.Multi-modalityimagingontrack.Eur J Nucl Med Mol Imaging 2007; 34:1410-1414.

Conferences• Kacperski,K.,Hutton,BF. Optimalparallelholecollimatorforcardiacimagingwithiterativereconstructionand

resolution recovery. Proc 3D Image Reconstruction in Radiology and Nuclear Medicine 2007; pp174-177.• Martin,S.,Hutton,B.SegmentingandTrackingDiaphragmandHeartRegions inGated-CTDatasetsasanAid

to Developing a Predictive Model for Respiratory Motion-Correction. Proc IEEE Nuclear Science Symposium and Medical Imaging Conference, 2007.

Papers• Barnden,L.R.,Dickson,J.,Hutton,B.F.(2006).Detectionandvalidationofthebodyedgeinlowcountemission

tomography images. Comp Methods and Programs in Biomed 84, 153-161. • Groves,A.M.,Kayani,I.,Syed,R.,Hutton,B.F.,Bearcroft,P.P.,Dixon,A.K,Ell,P.J.(2006).Aninternationalsurvey

of hospital practice in the imaging of acute scaphoid trauma. Am J Roentgenology 187, 1453-1456.• Hutton,B.F.,Olsson,A.,Som,S.,Erlandsson,K.,Braun,M.(2006).Reducingtheinfluenceofspatialresolutionto

improve quantitative accuracy in emission tomography: a comparison of potential strategies. Nucl Instruments and Methods in Phys Res A (569), 462-466.

• Kacperski,K.,Spyrou,N.M.(2006).Performanceofthree-photonPETimaging:MonteCarlosimulations.Physics in Medicine and Biology 50, 5679-5695. ISSN: 0031-9155.

• Pilowsky,L.S.,Bressan,R.A., Stone, J.M.,Erlandsson,K.,Mullingan,R.S.,Krystal, J.H. andEll,P.J. (2006).Firstin vivo evidence of an NMDA receptor deficit in medication-free schizophrenic patients. Molecular Psychiatry 11, 118-119. ISSN: 1359-4184.

• Stone,J.M.,Arstad,E.,Erlandsson,K.,Waterhouse,R.N.,Ell,P.J.,Pilowsky,L.S.(2006).[(123)I]TPCNE-AnovelSPET tracer for the sigma-1 receptor: First human studies and in vivo haloperidol challenge. Synapse 60(2), 109-117.

• Stone,J.M.,Erlandsson,K.,Arstad,E.,Bressan,R.A.,Squassante,L.,Teneggi,V.,Ell,P.J.PilowskyL.(2006).Ketaminedisplacement of [123I]CNS-1261 – a novel NMDA receptor SPET probe. Nuclear Medicine and Biology 33(2), 239-243. ISSN: 0969-8051.

Conferences• Martin,S.,J.,Hutton,B.,F.(2006).Quantificationofattenuation-correctionartefacts incardiacPET/CTcausedby

respiratory motion. Proc Medical Image Understanding and Analysis Ed: Graham J, Thacker N, Cootes T. pp95-99.• Nunez,M.,Kacperski,K.,Hutton,B.F.(2006).QuantitativelungSPECTwithouttransmissionacquisition:potential

applications in 3D patient-specific dosimetery and in evaluation of treatment response. Proc Int Conf on Quality Assurance and New Techniques in Radiation, IAEA, Vienna.

We would like to thank all the funding bodies and companies involved who make our research projects possible: