921

Dr. Jasjit Suri, is an innovator, scientist, a visionary, industrialist and an internationally known world leader in Biomedical Devices and Biomedical Imaging Sciences – applied to Diagnostics and Therapeutics. He worked as Scientist, Manager; Sr. Director, Vice President and Chief Technology Officer (CTO) level positions with several million dollar industries like IBM, Siemens Medical, Phillips Medicals, Fisher and Eigen Inc., companies.

He has written over 300 publications, 60 innova-tions (patents), 4 FDA clearances, over 20 books in medical imaging and biotechnologies (diagnostic and therapeutic) and has lead as leadership role in releasing products in the men’s and women’s mar-ket applied to the fields of: Cardiology, Neurology (Image Guided Brain Surgery and Spinal Surgery), Urology (Image Guided Prostate Biopsy and HIFU for BPH), Vascular (Atherosclerosis- MR and Ultrasound), Ophthalmology (Thermal Imaging) and Breast Cancer (MR, X-ray-Ultrasound Fusion Guidance) markets.

He received his MS in Neurological MRI from Univ. of Illinois, Chicago, USA, PhD in Cardiac Imaging from University of Washington, Seattle, Washington, USA, and MBA from Ivy League Weatherhead School of Management, Case Western Reserve University, Cleveland, USA. He was crowed with President’s Gold Model and Fellow of American Institute of Medical and Biological Engineering by National Academy of Sciences, DC. He has won over 50 awards during his career. Dr. Suri is also Strategic Advisory Board Member of over half a dozen industries and International Journals in Biomedical Imaging and Technologies. He main interests are cancer imaging for diagnosis and therapeutic applications for men’s and women’s market.

Editor Biographies

Jasjit S. Suri et al. (eds.), Atherosclerosis Disease Management, DOI 10.1007/978-1-4419-7222-4, © Springer Science+Business Media, LLC 2011

922 Editor Biographies

Dr. Chirinjeev Kathuria

Dr. Kathuria holds a Bachelor of Science (B.Sc.) degree and specialized in US Health Care Policy and Administration and a Doctor of Medicine (M.D.) from Brown University. He also holds a Master’s in Business Administration (M.B.A.) from Stanford University. Dr. Chirinjeev Kathuria, M.D., M.B.A. has measurable success in building businesses that impact world economies and shift business models. Dr. Kathuria has cofounded and helped build many businesses, which have gener-ated shareholder wealth and jobs. Dr. Kathuria and affiliated companies have been featured in many TV shows and media publications. Dr. Kathuria has extensive experience in the health-care industry and has consulted to a broad range of organizations in the USA, Europe, and Asia. He helped develop Arthur D. Little biotechnology and health-care policy practice in Europe. He conducted a compara-tive analysis of the European and US biotechnology industries resulting in a paper entitled “Biotechnology in the Uncommon Market” which was published in Biotechnology magazine in December 1992 which helped change at that time the current thinking of biotechnology development. Dr. Kathuria’s coauthored papers include “Selectivity Heat Sensitivity of Cancer Cells,” “Avascular Cartilage as an Inhibitor to Tumor Invasion,” and “Segmentation of aneurysms via connectivity from MRA brain data” the latter was published in the Proceedings of the International Society for Optical Engineering in 1993.

Dr. Filippo Molinari

Dr. Filippo Molinari received the Italian Laurea and the Ph.D. in Electrical Engineering from the Politecnico di Torino, Torino, Italy, in 1997 and 2000, respectively. Since 2002, he has been an assistant professor on the faculty of the Department di Electronics, Politecnico di Torino, where he teaches biomedical signal processing, biomedical image processing, and instrumentation for medical imaging. On March 2009 he was visiting professor at the University of Nagoya, Japan. He is the responsible for the image processing group at the BioLab of the Politecnico di Torino. Dr. Molinari’s main research interests include the analysis of

923Editor Biographies

strongly nonstationary biomedical signals and the medical imaging applied to the computer-aided diagnosis. Dr. Molinari developed several signal and image pro-cessing algorithms, especially in the field of neurology, neurosciences, and in the functional assessment of disabled subjects. Specific interests of Dr. Molinari’s research are early diagnosis, therapy, and rehabilitation. In the last 5 years, Dr. Molinari’s activity was focused on ultrasound imaging in the field of neurology and cardiology. Dr. Molinari is on the Editorial Board of the Journal of NeuroEngineering and Rehabilitation and acts regularly as reviewer for more than 20 international journals in the field of biomedical engineering and medicine. He has published more than 20 technical papers and has written a collaborative book on advances in diagnostic and therapeutic ultrasound. He is member of the Italian Group of Bioengineering, of the IEEE Engineering in Medicine and Biology Society (EMBS) and of the American Institute of Ultrasound in Medicine (AIUM).

925

AAbbott, A.L., 504Accelerated atherosclerosis.

See Iatrogenic conditionAccurate unsupervised segmentation

cylinder matching, 4133D connectivity filter, 414deformable model, 412Gibbs model, 413modified EM

conditional Lagrange maximization, 426

definition, 413DG weights, 426log-likelihood, 425, 426misclassification rate, 427relaxation MM-process, 426

multiscale filtering, 400natural TOF-and PC-MRA images

Chung–Noble’s segmentation, 417, 420–422

3-class LCDG model, 417, 419Gaussian mixtures, 416, 417scaled-up absolute deviations,

417, 418total absolute difference, 420Wilson–Noble’s segmentation, 417,

420–421phantoms

3D geometrical phantoms, 422, 423

erroneous voxels, 422error statistics, 423, 424ground truth, 421, 422qualitative visual analysis, 421Wilson–Noble’s and Chung–Noble’s

segmentation, 422–424Picker 1.5T Edge MRI scanner, 416scale-space filtering, 412

sequential EM-based initialization, 424–425

slice-wise segmentation, LCDG modelsBayesian probability, 415cumulative Gaussian probability

function, 414K-modal, 414, 415probability distribution, 414–415Q-ary intensities, 414segmentation algorithm, 415–416

ACEIs. See Angiotensin-converting enzyme inhibitors

Activated clotting time (ACT), 538Active contours (snakes)-based

segmentationbrightness normalization and

despeckling, 299damping force, 297global energy function definition,

296–297lumen–intima and media–adventitia

layers, 298MSE, 299multiresolution analysis, 297parametric contour representation, 296

Acute myocardial infarction, 377Agaston, A.S., 393Ajduk, M., 391Alberola-Lòpez, C., 312Allam, A.H., 5Altaf, N., 508Angiotensin-converting enzyme

(ACE), 605Angiotensin-converting enzyme inhibitors

(ACEIs), 607, 608Angiotensin II type 1 receptor blockers

(ARBs), 607, 608Anitschkow, N., 26Annexin A5 scintigraphy, 513–514

Index

926 Index

Antihypertensive drugsblood pressure lowering, 610–612cIMT, 613–615first-line antihypertensive drugs

ACEIs and ARBs, 607, 608Ca2+ channel blockers, 608CCBs and BBs, 607, 608health outcomes, 609–610HOPE trial, 608international guidelines, clinical

management, 607hypertension and atherogenesis, 602–604renin-angiotensin system,

antiatherosclerotic drugsACE2, 605angiotensinogen, 604–605AT1 and AT2, 605–606bradykinin, 606–607

Antoniades, C., 282Aoki, S., 472Arauz, A., 512ARBs. See Angiotensin II type 1 receptor

blockersArrhythmias, 43, 44Arterial calcifications detection

arterial plaques detection, in vivo, 684–687contrast enhanced vibro-acoustography,

687–688excised human carotid arteries, 681–682normal arteries, in vivo imaging, 682–684

Arterial plaque characterization techniquesCT plaque imaging, 197IVUS imaging, 198MRI, 197–198

Aschoff, L., 26Atherosclerotic carotid plaque, 382Atherosclerotic disease

complicated atheromasic injuriescalcification, 80ulcers and plaque rupture, 81–82

elementary atheromasic injuriesfatty streak diagram, 75, 76fibroatherosclerotic plaque, 78fibrous plaques, 75intimal hyperplasia, 75–77ulceration, 75, 77

epidemiology, 72etiopathogenetic theories, 82–83fibrous capsule and prognostic significance,

relationship, 79–80Mönckeberg sclerosis, 71normal anatomy, arterial vascular wall,

73–74risk factors, 72–73

ATL HDI-3000 ultrasound scanner, 165

BBalloon, 182, 183Bank, A.J., 89Barker, A.J., 910Bartlett, E.S., 376Bassiouny, H.S., 20, 389b-blockers (BBs), 607, 608Beard, P.C., 806Beck, J., 134Bernoulli’s equation, 91–92Beswick, J.P., 46Biologic nanoparticles and vascular disease

arterial calcification, 749biochemical characterization, 751–753history, 750–751infection, 750microparticles, 757origin and life forms, 753–754transmissible cause of disease, 754–756

B-mode ultrasonographyintima/media thickness, 460–461limitations, 460molecular contrast-enhanced

ultrasonography, 462–463plaque echogenicity, 461plaque irregularity, 462primary screening tool, 460

Boissel, J.P., 576Brathwaite, A., 198Briley-Saebo, K.C., 481Brusseau, E., 770Burckhardt, C.B., 154Butterworth filter, 141, 161

CCABG. See Coronary artery bypass graftingCai, J.M., 389, 443, 444, 464, 506Calcified nodule, 15–16Calcium-channel blockers (CCBs), 607, 608CALEXia. See Completely automated layers

extraction based on integrated approach

Callahan, R.J., 365Capineri, L., 890Cappendijk, V.C., 507Cardiovascular disease (CVD), 564–565

assessment, 283atherosclerosis, 282complement system, 649–651IMT monitoring, risk marker, 286plaque analysis, 286risk assessment, 222

Cardiovascular riskasymptomatic CVD, 39

927Index

carotid diseaseCAD vs. CVD, 38subclinical atherosclerosis, 39

carotid endarterectomy, 41–43controversies, CHD testing, 43–45IMT

cardiac events, 45–46coronary angiography, 45

patients with TIA/stroke, 39–41Carotid artery

B-mode ultrasound images, 254, 256, 265imaging

diagnostic flowchart, 365–366DSA, 366–367FDG-PET, 371–372MRA, 369–371scintigraphy, 373SPECT, 372–373US-ECD, 367–369

pathology and stroke riskdistal embolization, 374hypo-perfusion, 374left mono ocular symptoms, right

hemiplegy and dysesthesia, 373MDCTA, 375–376MRA, 375NASCET, ECST and ACAS evaluation,

374–375TIA/minor stroke, 377US-ECD, 375

ultrasound image segmentationinter-greedy performance samples,

269–272LI performance evaluation, 266–268MA performance evaluation, 266, 268–270system errors, 266

wall thickness, 395–396Carotid artery atherosclerosis. See Carotid

artery stenosisCarotid artery bifurcation ultrasound images,

despeckle filteringadditive noise, 157–158anisotropic diffusion filtering (DsFad), 162–163application, 187–188atherosclerotic plaque characterization, 166coherent nonlinear anisotropic diffusion

filtering (DsFnldif), 163–164evaluation protocol, 187geometric filtering (DsFgf4d), 160–161homomorphic filtering (DsFhomo), 161–162image quality evaluation metrics, 178, 179intima media complex and plaque segmentation

CCA, 181–183longitudinal ultrasound B-mode image,

183, 184

local statistics filteringfirst order statistics filtering

(DsFlsmv, DsFwiener), 158–159homogeneous mask area filtering

(DsFlsminsc), 159–160maximum homogeneity, pixel neighborhood

filtering (DsFhomog), 160median filtering (DsFmedian), 160methodology

despeckle filtering, 165distance measures, 166–167image quality evaluation metrics,

167–169material, 165statistical kNN classifier, 167texture analysis, 166ultrasound images recording, 165univariate statistical analysis, 167visual evaluation, 169–170

speckle definition, 154speckle noise model, 157, 158speckle reduction techniques, 155symptomatic ultrasound image and cardiac

image, 170–172texture analysis

distance measures, 171–176kNN classifier, 173, 177–178univariate statistical analysis, 173–176

validation result, 183, 185visual evaluation, 178, 180–181wavelet filtering (DsFwaveltc), 164–165

Carotid artery longitudinal ultrasound images2-D B-mode ultrasound image, 222–223CALEXia

advantages, real image database, 244–245

CCA automatic recognition (see Common carotid artery)

IMT measurement strategy, 234–237non-perfect adventitial tracings,

conditions, 245–246performance improvement,

247–248performance limiting factors, 247

CULEXsa, 224performance evaluation and

benchmarkingCA automated tracing, 240–242carotid wall segmentation and IMT

measurement, 242–244performance metric design

image database, 236–237IMT metric, 239–240mean system error, 239PDM, 237–239

928 Index

Carotid artery stenosisclinical symptoms

postsurgery evaluation, 820presurgery evaluation, 819–820

ex vivo molecular staining techniquesDNA microarray, 852, 853MMPs, 851protein microarray, 852

limitations, 866, 868multimodal molecular imaging

atherosclerosis and angiogenesis imaging, 860–861

color mapping techniques, 854concepts, 852, 8543D echographic data segmentation,

866, 8673D imaging, nanoparticles, 861–863FDG-PET/CT and MRI, 854imaging principles and techniques,

854–857MALDI imaging technique, 862–865microfluidics, 865–866nanoparticles, 858–860

nanotechnology, 868postsurgery evaluation

carotid artery tissue processing, 846CEA procedure, 845–846endarterectomy specimen, 839, 842–845histopathology and MRI, 849–851MRI (1.5T), ex vivo, 847–849MRS (9.4T), ex vivo, 847, 848plaque components, discriminative

analysis, 849–850plaque histopathology classification, 845surgical procedures, 841–842

presurgery evaluationACC/ACR classification, 826, 828–830aggressive statin treatment, 840–841blood biomarkers, 820–821boundary detection, 829, 838–839CAD evaluation components, 823, 824carotid endarterectomy, 830, 831dyslipidemia, 820imaging modalities, 826, 827lesion components and values, 837–838MRM and MRI (1.5T), in vivo, 834–835multiple contrast technique, 835patient selection criteria, 819, 821, 832plaque, atherosclerosis process, 821plaque development process, 821–822post-surgery diagnostic criteria, 821segmentation, 836TE and TR selection, 836–837T1-w/T2-w/PD-w techniques, 839–840

in vivo imaging techniques, 823–826in vivo MRI images, statistical methods,

832–833stroke, 818

Carotid atherosclerosiscarotid endarterectomy specimen, 5–6carotid vs. coronary disease, 17–18classification

AHA classification scheme, 7–8limitation, AHA, 8–10

imaging modalitiesCT angiography, 23digital subtraction angiography, 22Doppler ultrasound, 22–23inflammation, 25–27MRI, 23–25

ischemic stroke, 4pathologic features

advanced symptomatic lesions, 11–16early, asymptomatic lesions, 10–11lesions with thrombi, 14–16stable atherosclerotic plaque, 16–17

plaque localization, 6–7quantification

total plaque volume (TPV), 331, 332vessel wall volume (VWV),

331, 333, 334regression monitoring

3D and 2D carotid map generation, 337–340

mapping spatial and temporal changes, 340–343

stroke risk, 331TPA measurements, intensive statin

treatment, 334–336VWV measurements, intensive statin

treatment, 335–337risk factors, 18–19symptomatic vs. asymptomatic patients,

19–22Carotid bifurcation, 6–7Carotid duplex ultrasonography (CDUS),

824–825Carotid endarterectomy (CEA), 5, 41–43

anesthesiological technique, 535, 537CABG, 546–547carotid artery pathology and stroke risk, 374postsurgery

carotid artery tissue processing, 846endarterectomy specimen evaluation,

839, 842–845histopathology and MRI, 849–851MRI (1.5T), ex vivo, 847–849MRS (9.4T), ex vivo, 847, 848

929Index

plaque components, discriminative analysis, 849–850

plaque histopathology classification, 845procedure, 845–846selection criteria, 842–843

surgical technique, 540, 541symptomatic and asymptomatic carotid

stenosis, 534Carotid intima–media thickness (cIMT)

antihypertensive drugs, 613–615biomarkers and surrogate endpoints, 578inter-greedy technique

atherosclerotic process, 254CALEXia architecture, 258–259CALEXia, CULEXsa, WS, and IG

algorithm, 266, 274–275cardiovascular disorders, risk marker, 254CULEXsa architecture, 256–258EPV, 273–274IG performance samples, 269–273lumen-intima (LI) performance

evaluation, 266–268MA and LI tracing accuracy, 255media-adventitia (MA) performance

evaluation, 266, 268–270multiple image processing boundary

fusion, 262–264performance evaluation metrics and

image dataset, 264–265system errors, 266WS transform, 259–262

ultrasound trials, 585–587Carotid plaque enhancement (CPE), 394–395Carotid stenosis treatment

anesthesiological techniqueACT, 538CEA, 535, 537cerebral perfusion, 536EEG, 535–536intraoperative stump pressure measure,

538, 539jugular mixed venous O

2 saturation, 536

local anaesthesia, 538NIRS, 536transcranial Doppler, 536

CEA/CABG, 546–547computed tomography angiography, 533duplex scan

anechoic plaque, 530, 531calcific plaque, 530, 531definition, 530echolucency and echogenicity, 530hypoechoic plaque, 531, 532irregular/ulcerated plaque, 531, 533

endovascular techniquecarotid stenting technique, 548–549clinical results, 555common carotid artery access, 549–550diagnostic catheter, 549interdisciplinary collaboration, 555perioperative complications, 555–556pharmaceutical protocol, 553–555protection systems, 550–552stent implantation, 552–553vascular access, 549

magnetic resonance imaging, 532NASCET, ECST and ACAS, 530, 533post-traumatic depression, 529quality check, 543–544shunt, 543surgery results, 547–548surgical technique

CCA, 539–540ECA and ICA, 539–540eversion technique, 542–543IJV, 539SCM, 539standard CEA, 540, 541

symptomatic and asymptomatic carotid stenosis

angiography, spiral CT and angioMR scan, 534, 536

artery morphology and plaques, 534, 535carotid plaque types, 535, 537CEA, 534

TIA, 530, 534urgent surgery, 544–546

Carotid ultrasound images, intima-media thickness measurement

carotid wall evolution, 286–287carotid wall segmentation

active contours (snakes)-based segmentation, 296–299

3-D segmentation methods, 306–307dynamic programming techniques,

295–296edge tracking and gradient–based

techniques, 291, 293–295HT, 304–305instrumental variability, 289integrated approach, 305–306IVUS techniques, 411–413local statistics and snakes, 299–302Nakagami modeling, 302–304noise sources, 289–291normal and pathology, biological

variability, 288–289CCA, 282–283

930 Index

Carotid ultrasound images, intima-media thickness measurement (cont.)

computer measurements and CVD, 286human tracings, correlation

HD, 310MAD, 309–310manual and computer-measured IMT, 313PDM, 311–312percent statistic test, 312–313

supra-aortic circulation, 283vessel wall segmentation, 283–286

3D Carotid ultrasound imagingcarotid atherosclerosis (see Carotid

atherosclerosis)IMT, 326–327manual segmentation, 20scanning technique

cube view approach, 330image reconstruction, 330magnetically-tracked free-hand

scanners, 327mechanical linear scanners, 327–329TPA and VWV, 327

Cassius, Dio, 4CCA. See Common carotid arteryCCBs. See Calcium-channel blockersCDUS. See Carotid duplex ultrasonographyCEA. See Carotid endarterectomyCerebral oximetry, 536Cerebrovascular disease (CVD), 37CE US. See Contrast-enhanced

ultrasonographyCFM. See Color flow mappingChalana, V., 312CHD. See Coronary heart diseaseCheng, D.C., 243, 292, 297, 298, 313Cheng, G.C., 88, 89, 91Chin, 182–184, 186Chiu, B., 341Chlamidia pneumnoniae, 83Chronic total occlusion, 17Chu, B., 467Chung, A.C.S., 413Chung–Noble’s segmentation, 417, 420–424cIMT. See Carotid intima–media thicknessCinthio, M., 777–780Coli, S., 502Color flow mapping (CFM), 886–888Common carotid artery (CCA), 93, 499, 500,

539–540anatomical view, 223automatic recognition

column-wise approach, 225line segments (see Line segments)seed points selection, 225–228

B-Mode image, 300Hough transform, 304–305integrated approach, 305–306Nakagami modeling, 302–304snake-based segmentation techniques,

297–299vessel wall segmentation, 283–286wall points identification, 291, 293

Completely automated layers extraction based on integrated approach (CALEXia)

advantages, real image databasemedia-adventitia (MA) segmentation

error, 244real-time implementation, 245suitability, carotid morphologies, 244user independence, 244

architecture, 258–259CCA automatic recognition (see

Common carotid artery)IMT measurement strategy, 234–236

EPV, 273–274LI segmentation technique, 267–268MA segmentation technique, 268–270mean IMT measurement error, 266,

274–275mean system error, 264non-perfect adventitial tracings, 245–246performance improvement, 247–248performance limiting factors, 247

Completely user-independent layers extraction (CULEX)

algorithm, automated segmentation, 211ceUS image processing, 203–204segmentation and GT comparison, 205–207ultrasound images segmentation strategy,

201–202Completely user-independent layers extraction

algorithm based on signal analysis (CULEXsa), 224

architecture, 256–258EPV, 273–274IMT measurement errors, 266, 274–275mean system error, 264

Computational fluid dynamics (CFD), 98–99Computed tomography (CT)

angiography, 23plaque imaging, 197

Computed tomography angiography (CTA)advanced vascular imaging, 353carotid artery (see Carotid artery)image reconstruction software, 354plaque (see Plaque)post processing techniques

contrast material, 364CPR, 358–359, 361

931Index

MIP and MPR, 358–360opacity, 363projectional and perspective

methods, 357radiation dose, 364–365raycasting, 364transverse and in-plane resolution, 357voxel selection, 363VR, 361–362

principles4-detector-row scanners, 355mathematical image reconstruction, 354MDCTA, 355–356scanning parameters, 356single detector-row scanners, 355third-generation geometry, 355

spatial and temporal resolution, 3543D Connectivity filter, 414Continuous wave (CW) Doppler, 881–883Contrast-enhanced ultrasonography

(CE US), 502advantage, 213B-mode imaging

color-coded image, after analysis and tissue characterization, 197–199

CULEX and manual segmentation comparison, 205–206

CULEX segmentation, plaque, 204–205image after analysis and tissue

characterization, 205–206image enhancement, 204–205processing strategy, 203–204wall tissue enhancement, 203

CULEX automated segmentation, 211limitation, 212plaque characterization and histology

plaque with calcium deposits, 207–209soft unstable plaque, 209–211

Coronary artery bypass grafting (CABG), 546–547

Coronary artery disease (CAD), 37Coronary atherosclerosis, 38Coronary heart disease (CHD), 37

CVD, 565fibrates, 594, 595lipoprotein cholesterol retention, arterial

intima, 572Corti, R., 467, 475, 478, 589CPE. See Carotid plaque enhancementC-reactive protein (CRP), 18Crimmins, T.R., 185Cross-validation approach, 146CTA. See Computed tomography angiographyCULEX. See Completely user-independent

layers extraction

CULEXsa. See Completely user-independent layers extraction algorithm based on signal analysis

CVD. See Cardiovascular disease

DDaubenchies Symlet wavelet, 164Daugman, J.G., 138Davies, J.R., 512DeBakey, M.E., 530De Korte, C.L., 767Delsanto, S., 202, 292, 299–301, 313Destrempes, F., 243, 292, 302, 3104-Detector-row scanners, 355Devereaux, P.J., 44de Weert, T.T., 379, 382, 384, 504, 505Digital subtraction angiography (DSA), 23,

366–367, 458Discrete wavelet packet frames (DWPF), 1343D MRA. See Accurate unsupervised

segmentationDNA microarray, 852, 853Donoho, D.L., 156, 164, 186Doppler, J.C., 880Doppler ultrasound, 22–23Drug therapy, atherosclerosis

antihypertensive drugs (see Antihypertensive drugs)

apoptosis, plaque rupture, and thrombus formation, 574–575

artery diseases, 563–564atherogenesis, 568atheroma lesions, 566atherosclerotic plaques, 567atherothrombosis, 567–568biomarkers and surrogate endpoints

carotid B-mode ultrasound, 578cIMT, 577clinical and statistical characteristics, 576coronary intravascular ultrasound,

578–579gold standard, 575MRI, 579–580plaque volume, 577QCA, 577

cardiovascular morbidity and mortality, 564CVD, 564–565endothelial dysfunction

cardiovascular risk factors, 568–569characteristics, 569gold standard test, 570noninvasive tests, 570ROS, 569shear stress, 568

932 Index

Drug therapy, atherosclerosis (cont.)hypolipidemic drugs (see Hypolipidemic

drugs)lipoprotein cholesterol retention, arterial

intimaCHD, 572cholesterol transport and metabolism,

570, 571chylomicrons, 570–571HDL-C levels, 573hypercholesterolemia, 570LDL-C levels, 572–573lipid triad, 573VLDL, 570–571

primary and secondary prevention, 566proinflammatory oxidized LDL, 573–574risk factors, 565–566

DSA. See Digital subtraction angiographyDunmire, B., 889Duplex ultrasonography, 45

EECA. See External carotid arteryEliasziw, M., 375EPV. See Error per vertexErosive endothelial damage, 82Error per vertex (EPV), 273–274Error summation, Minkowski metric, 168Espeland, M.A., 576, 585External carotid artery (ECA), 539–540

FFaita, F., 274, 292, 295, 315Fast Fourier transform (FFT), 156, 161FDG. See Fluorine-18-labeled 2-deoxy-d-

glucoseFDG-PET. See [18F]-fluorodeoxyglucose

positron emission tomographyFDTA. See Fractal dimension texture analysisFeasby, T.E., 548Fell, G., 368Fenster, A., 306, 307[18F]-fluorodeoxyglucose positron emission

tomography (FDG-PET), 371–372, 512–513

FFT. See Fast Fourier transformFibroatherosclerotic plaque, 78Fibrocalcific plaques, 17Fibrous cap atheroma, 11Fibrous capsule, 78Fibrous plaques, 76Finite element method (FEM), 89

First-order absolute moment edge operator (FOAM), 295

First-principle stress (FPS), 107–108

Fisher, C.M., 457Fluid structure interaction

vs. 3D structure-analysis, 91–92simulation and boundary conditions

CCA, 97fluid flow parameters, 98–99

stress analysisblood flow patterns and wall stress, 90lipid core volume and fibrous cap

thickness, 108–110with multiple patients, 99–106with TIA patients, 106–108

Fluorine-18-labeled 2-deoxy-d-glucose (FDG), 476–477, 483

Fluoroscopic X-ray system, 125Folk, R., 750Fourier power spectrum (FPS), 166, 178Fractal dimension texture analysis (FDTA),

166, 178Frayne, R., 908, 909Frost, V.S., 154–157Frydrychowicz, A., 910Füst, George, 649

GGAE. See Geometric average errorGeertinger, P., 649Geometric average error (GAE),

168, 178Geroulakos, G., 45Giannoni, M.F., 502Glagov, S., 724GLDS. See Gray level difference statisticsGlomset, J.M., 635Golemati, S., 223, 304Golledge, J., 21, 389Gongora-Rivera, F., 38Goodman, J.W., 154Gould, A.L., 591Gradenigo Hospital, 199–200, 213, 265Gray level difference statistics (GLDS),

166, 178Gray-scale median (GSM), 499–500Groen, H.C., 113Grogan, W.E., 530Grønholdt, M.L., 499Grotenhuis, H.B., 910GSM. See Gray-scale medianGutierrez, M.A., 292, 297

933Index

HHaider, N., 739Hansen, H.H.G., 775Hansen, K.L., 898Haralick, R.M., 166Hardie, A.D., 393Harloff, A., 910Hatsukami, T.S., 445, 464Hausdorff distance (HD), 310HDL. See High-density lipoproteinsHealed plaque ruptures (HPRs), 16Heart and coronary arteries, 122, 123Heart Outcome Prevention Evaluation

(HOPE), 608Heat shock proteins (HSP), 641Hematoxylin and eosin (H&E), 126Hemodynamics, cardiovascular systems

echo PIVbasic principle, 899carotid bifurcation model, 900, 902hemodynamics quantification, 901–903microbubbles, 899–901optical PIV, 904–905vortex flow parameters, 904waveforms comparison, 900–902

PC-MRIcarotid, local flow imaging, 911–912developments, 910–911global flow parameters, 907–909local flow parameters, 909methodology, 905–907

speckle trackingcorrelation search algorithm, 892cross-correlation method, 893PWE, 893–895

transverse oscillation, 896–898ultrasound Doppler

color flow mapping, 886–888continuous wave Doppler, 881–883pulsed wave Doppler, 883–886vector Doppler, 888–892

WSS, 879–880Hermans, M.M., 285High-density lipoproteins (HDL),

570–571, 573High-resolution multicontrast magnetic

resonance imagingclassification, 443–445computer-based three-dimensional

analysis, 450fibrous cap status and lipid core,

444–447hemorrhage, 447–449image resolution, 450

limitations, 451MRI 3D surface rendering, 450, 451USPIO, 450

Hill, J.H., 648Hill’s criteria, 756, 758Hodgson, Joseph, 25Holzapfel, G.A., 91Hough transform (HT), 304–305HT. See Hough transformHyperfibrinogenemia, 19Hyperhomocysteinemia, 72Hyperlipemia, 72Hypolipidemic drugs

bile acid sequestrants, 599–600characteristics, 580, 581cholesterol absorption inhibitors,

600–602fibrates

atherogenic dyslipidemia, 593CHD, 594, 595chylomicronemia, 594gallstones, 596HDL-C and LDL-C levels, 593VA-HIT trial, 594

nicotinic acidARBITER 2 trial, 598dyslipidemia, 599FFA levels, 596GPR109A, 596, 598GPR109B, 596HATS trial, 598hyperglycemia, 599LDL and HDL levels, 596, 598multiple tissue enzymes and receptors,

596, 597VLDL levels, 596

statinsangiographic trials, 584–585clinical outcomes, 582, 583coronary intravascular ultrasound, 585,

588, 589LDL receptors, 580magnetic resonance imaging,

589–590pleiotropic effects, 590–593primary prevention, 584secondary prevention, 582, 584ultrasound trials, cIMT biomarkers,

585–587

IIatrogenic condition, 75ICA. See Internal carotid artery

934 Index

Image analysisfluorescence images, 740image intensity, 741lesion regression, 739optical imaging, 740PET imaging, 740–741ultrasound imaging, 740

Imaging modalitiesCT and MR imaging, 739nuclear and optical imaging, 738resolution and sensitivity, 736, 737ultrasound imaging, 736

Imparato, A.M., 458IMT. See Intima-media thickness (IMT)Inflammation control, atherosclerosis

preventionanimal models, 635–636complement system

alternative pathway, 645C3a, C4a, and C5a anaphylatoxins,

647–648cascade, 642–644C57BL mouse model, 651–653classical pathway, 643–645complement inhibitors, 646CVD, 649–651historical notes, 648–649lectin pathway, 645membrane attack complex, 646mode of action, 654–656myocardial infarction, 648reperfusion injury, 656–658VCP, 647

HSP, 641initiation and progression, 636–637LDL and lipid transport, 639–641lesion development stages, 635myocardial infarction, 637–638pathogenesis, 633–634risk factors, 638–639

Internal carotid artery (ICA), 499, 500, 539–540

Internal jugular vein (IJV), 539Inter-slice distance (ISD), 340Intimal hyperplasia, 75–77Intimal xanthoma, 10–11Intima-media thickness (IMT), 45–46, 59

artery wall segmentation, 284–285cardiovascular and cerebrovascular risk

indicator, 282carotid artery sample, echographic

appearance, 285–2863D carotid ultrasound imaging, 326–327cerebrovascular events, 436, 438

computer-assisted automatic measurement, 435, 437

CVD risk assessment, 222definition, 435leading edge method, 435, 436measurement, 181–182

Gaussian kernel, 234image segmentation, 235–236schematic representation, segmentation,

234–235progression and regression, 436, 437risk factor-modifying therapy, 436young populations, 437

Intraplaque hemorrhage (IPH), 466–467Intravascular photoacoustic (IVPA) imaging

angioscopy, 789benchtop imaging system, 789, 790combined IVUS/IVPA imaging, 790–791,

805–810ex vivo artery imaging, 789integrated catheter design

Beard’s probe design, 806light delivery system, 805–808optical fiber bundle design, 808, 809phantom images, 808, 809prototypes, 806–807ultrasound array and fire fiber, 808–810

laser fluence, 789molecular and cellular specific

IVPA imagingatherosclerosis-related biomarkers, 795contrast agents, 796macrophages, atherosclerosis animal

model, 799–801macrophages, Au NPs, 796–798

optical absorption coefficient, 789spectroscopic IVPA imaging

correlation based approaches, 795first derivative, 792–793lesions composition, 793–794multi-wavelength, photoacoustic

response, 793optical absorption spectra, 792rabbit aorta samples, 794

stent deployment3D image construction, 802, 805malapposition, 801MRI, CT and OCT, 802rabbit aorta, 802–805stenting procedure and

post-surgery, 801stents vs. vessel structure, 803, 804tissue-mimicking phantom, 802–803

vessel-mimicking phantom, 790, 791

935Index

Intravascular ultrasound (IVUS) imaging, 577–579, 788–789

arterial plaque characterization techniques, 198

atherosclerotic tissue characterization algorithms

blood flow, 144–145consistency among PH images, 142, 143histological image interpretation,

145–146pressure change, 142, 144tissue classification, 146–147tissue signatures variability, 141–142

carotid wall segmentation, 411–413combined IVUS/IVPA imaging, 805–810neurological evaluation and

management, 56spectral and RF based approaches

IVUS-IB, 129–133IVUS-VH, 129–131spatial autocorrelation function, 128spectral signature, 128

techniques, 307–309texture based approaches

IVUS-ECOC, 136–138IVUS-IBH, 138–140IVUS-PH, 134–136

therapeutic procedure, 121ultrasound virtual histology, 287in vitro set-up and specimen preparation

histology matching problem, 124ROIs, 127

in vivo acquisition, 122–124IPH. See Intraplaque hemorrhageIVPA. See Intravascular photoacoustic

imagingIVUS. See Intravascular ultrasound imagingIVUS elastography (IVE), 131–133IVUS-error correcting output codes

(IVUS-ECOC), 136–138IVUS-image based histology (IVUS-IBH),

138–140IVUS-integrated backscatter (IVUS-IB)

color-coded maps, 130–132in vitro IVUS grayscale images, 131vs. IVUS-VH, 131, 133

IVUS-prognosis histology (IVUS-PH), 134–136IVUS-virtual histology (IVUS-VH), 129–131

JJahromi, A.S., 368Jensen, J.A., 896Jeremias, A., 136

KKafetzakis, A., 45Kanai, H., 772, 775Kasai, C., 887Katz, J., 832Kaufmann, B.A., 733, 736, 739Kelly, K.A., 733Kerwin, W.S., 509Kietselaer, B.L., 373, 513Kim, D.I., 380Kim, K., 774Kitamura, A., 462k-nearest-neighbour (kNN), 167, 173, 177–178Koch’s Postulates, 755–756, 758Kooi, M.E., 474, 510, 511Kovanen, P.T., 282Kuan, D.T., 154–156Kwee, R.M., 499–501, 503

LLai, 182–184, 186Lal, B.K., 204, 212LaMuraglia, G.M., 547Lancelot, E., 481Landesberg, G., 43Laplacian pyramid-based nonlinear diffusion

(LPND), 290Laufer, E.M., 282Law, M.R., 611Laws texture energy measures, 166, 177–178LCDG. See Linear combination of discrete

GaussiansLDL. See Low-density lipoproteinsLee, J.S., 154–157, 183Lee, R.T., 89Lee, S.J., 477Lemarie-Battle filter, 135Levy interdistribution distance, 420Liang, Q., 292, 296Liguori, C., 292–295Lima, J.A., 579, 589Linear combination of discrete Gaussians

(LCDG)3-class LCDG model, 417, 419initial LCDG model, 417, 418, 423–425slice-wise segmentation

Bayesian probability, 415cumulative Gaussian probability

function, 414K-modal, 414, 415probability distribution, 414–415Q-ary intensities, 414segmentation algorithm, 415–416

936 Index

Line segmentsfitting

combinability and validation, 229–230energy function, 228geometric features measurement, 228intersection energy, 230iterative line segment formation, 227–228linear discriminator, 227, 230sample image detection, 231–232

recognition and classification, 232–233Lin, W., 472Lipid-rich necrotic core (LRNC)

conventional MRI, 507, 508non-invasive imaging, carotid

atherosclerosis, 514–515Lisauskas, Jennifer, 127Li, Z.Y., 91, 389Lizzi, F.L., 129Lobregt, S., 297Local binary pattern (LBP binary), 140Local statistics and snakes

automatic detection, lumen points, 299–300fuzzy K-means classifier, 301snake formulation, 301user–independent segmentation,

carotid wall, 302Loizou, C.P., 287, 289–292, 299, 314Long, G.W., 547Loree, H.M., 388Lovett, J.K., 114, 382Low-density lipoproteins (LDL), 570–573LRNC. See Lipid-rich necrotic coreLucev, N., 288

MMAC. See Membrane attack complexMacKinnon, A., 503MAD. See Mean absolute distanceMagnetic resonance angiography (MRA)

carotid artery imaging, 369–370fibrous cap, 389

Magnetic resonance imaging (MRI), 23–25arterial plaque characterization techniques,

197–198biomarkers and surrogate endpoints,

579–580bright-blood technique, 61, 63conventional MRI

gadolinium-based contrast agents, 506, 507

hemorrhages and calcifications, 508LRNC, 507, 508multisequence non-CE MRI, 506, 508

pre and postcontrast T1-weighted TSE images, 506, 507

T1-weighted TFE images, 507, 5082D modeling, 91dynamic CE MRI, 509dynamic contrast-enhanced MRI and

neovascularisation, 472–473expansive remodeling, 467–468fibrous cap and lipid rich-necrotic core,

464–465fibrous cap disruption and platelet

aggregation, 465–466flow modeling, shear stress estimation,

469, 471FSI models, 92geometry reconstruction reproducibility, 92IPH, 466–467long image acquisition times, 463multi-contrast imaging, 93physiological loading condition, 96plaque criticity, 56plaque vulnerability, 92rupture, plaque morphology

information, 106SE-TSE technique, 60severity of stenosis, 466superficial calcified nodules, 468–470three dimensional (3D)

data acquisition, 463vs. ultrasound imaging, 56USPIO-enhanced MRI, 510–511USPIO-enhanced MRI and macrophage

content, 473–475virtual histology, 55

MALDI imaging technique, 862–865Malik, J., 162, 163Manca, G., 373Markl, M., 910Markus, H.S., 503Maroko, P.R., 657Masden, E, 270Mathias, K., 548Mathur, K.S., 38MATLAB, 212Matlab, 154, 157Matrix metalloproteinases (MMPs), 481, 851Maurice, R.L., 132, 772, 773Mauriello, A., 19Maximum-intensity projection (MIP),

358–360MDCT. See Multidetector-row computed

tomographyMean absolute distance (MAD),

309–310

937Index

Mean squared error (MSE), 156, 157, 168, 179, 299

Membrane attack complex (MAC), 646Metabonomics

analysis techniquescorrelation analysis, 705–706discriminant analysis, 707–708multi-way ANOVA, 706PCA, 706PLS, 707

and atherosclerosis, 700–701classification, 713, 715database and patients

hematochemical variables, 703, 704instrumental data, 702–703patient population, 701–702

database reduction, 708–710patient analysis

eigenvalues, 710, 711hyperplane, PCA subjects distribution,

710–712hyperplane, PLS subjects

distribution, 712original variables weight, 710, 711PCA components, 711PLS-DA classifier, 712–713

plaque typology, 713, 714Minimum-mean-square error (MMSE)

criterion, 156Mintz, G.S., 147MIP. See Maximum-intensity projectionMissel, E., 147Molecular and cellular specific

IVPA imagingatherosclerosis-related biomarkers, 795contrast agents, 796macrophages, atherosclerosis animal model,

779–801macrophages, Au NPs, 796–798

Molecular imagingcontrast agents, 734–736homing ligands

antibodies, 733competitive binding assays, 734interactions, 732–733SELEX process, 734short peptides, 733

image analysisfluorescence images, 740image intensity, 741lesion regression, 739optical imaging, 740PET imaging, 740–741ultrasound imaging, 740

imaging modalitiesCT and MR imaging, 739nuclear and optical imaging, 738resolution and sensitivity, 736, 737ultrasound imaging, 736, 738

molecular markersatheroma components, 725–726atherosclerosis animal models, 727characteristics, 724–725phage display technique, 726–727receptor identification, 727screening strategy, 726

potential molecular targetsadhesion molecules, 728–729apoptosis, 731expression pattern, 725, 727–728fibrin deposition and thrombus formation,

731–732neovessel formation, 729–730oxidized LDL and foam cells, 729proteolytic enzymes, 730–731

principles, 724Molinari, F., 265, 288, 289, 292, 301, 302,

305, 306, 314, 316Monney–Rivilin model (AnsysTM ), 97Montecucco, F., 282Moody, A.R., 391, 467, 507Moore, W.S., 458Moran, P.R., 907MPR. See Multi planar reconstructionMRA. See Magnetic resonance angiographyMRI. See Magnetic resonance imagingMSE. See Mean squared errorMultidetector-row computed tomography

(MDCT), 504–505Multi-detector row computed tomography

angiography (MDCTA)carotid artery imaging, 371–372fibrous cap, 390pathology and stroke risk, 375–376vs. US-ECD, 380–381

Multimodal molecular imagingatherosclerosis and angiogenesis imaging,

860–861color mapping techniques, 8543D echographic data segmentation,

866, 8673D imaging, nanoparticles, 861–863FDG-PET/CT and MRI, 854imaging principles, 854–856imaging techniques, 856–857MALDI imaging technique, 862–865microfluidics, 865–867nanoparticles, 858–860

938 Index

Multi planar reconstruction (MPR), 358, 360Multiscale filter, 412Multi-way ANOVA analysis, 705, 706, 709Munk, P., 896Myocardial infarction (MI), 41, 436

NNahrendorf, M., 738, 739Nair, A., 129, 136Nasu, K., 198Navone, Roberto, 76–78, 81, 82Near-infrared spectroscopy (NIRS), 536Neighbourhood gray tone difference matrix

(NGTDM)distance measures, 166–167, 171–173kNN classifier, 167, 173, 177–178texture features extraction, 166

Neurological evaluation and managementclinical examination, 59ethical issues, 58–59exclusion criteria, 58instrumental examinations, 59–60laboratory and hematochemical exams, 59objectives and end points

atheromasic plaque, 56IVUS, 56US vs. MRI, 56–57

patients inclusion criteria, 57–58results and impact

plaque composition, 65sensibility and specificity, plaque, 65

sample instrumental dataangio-MRI, 60–61bright-blood MRI characterization, 61, 63carotid endarterectomy, 62NASCET criterion, 61sonographic appearance, 62, 64

strokeischemic stroke, 53prevention and management, 54–56stenosis, 54

NGTDM. See Neighbourhood gray tone difference matrix

Noble, J.A., 413Non-invasive imaging

arterio-embolic strokes, 433B-mode ultrasound, 434–435carotid plaque, 434carotid plaques characterization, 498degree of stenosis, 437, 439echolucent plaque, 516high-resolution multicontrast MRI

classification, 443–444

computer-based three-dimensional analysis, 450

fibrous cap status and lipid core, 444–447hemorrhage, 447–449image resolution, 450limitations, 451MRI 3D surface rendering, 450, 451USPIO, 450

IMTcerebrovascular events, 436, 438computer-assisted automatic

measurement, 435, 437definition, 435leading edge method, 435, 436progression and regression, 436risk factor-modifying therapy, 436young populations, 437

ipsilateral TIA/stroke, 515LRNC, 514–515MDCT, 504–505MES positive symptomatic and

asymptomatic patients, 514MRI

conventional MRI, 506–508dynamic CE MRI, 509USPIO-enhanced MRI, 510–511

nuclear imaging techniquesannexin A5 scintigraphy, 513–51418F-FDG PET, 512–513

origin of stroke, 439–440plaque neovasularization, 500plaques morphology and texture

histology, 440, 441stable plaque, 442–443stages of atherosclerosis, 440–442

statin therapy, 516TCD, 502–504ultrasonography

CE US, 502conventional B-mode US, 499–501

vulnerable plaque, 498Non-invasive targeting,

vulnerable carotid plaquesB-mode ultrasonography

intima/media thickness, 460–461limitations, 460molecular contrast-enhanced

ultrasonography, 462–463plaque echogenicity, 461plaque irregularity, 462primary screening tool, 460

clinical trialsATHEROMA study, 480CEU techniques, 481

939Index

endovascular treatment, 477FDG-PET, 480high-risk plaques, 478, 479LRNC, 478, 480METEOR study, 480ORION study, 478, 480–481pravastatin, 478rosuvastatin, 478, 480simvastatin, 478statin treatment, 478VWA and VWT, 478

culprit plaques, 458diagnostic imaging methods, 459DSA, 458luminal stenosis, 458, 459MMPs, 481molecular imaging, 482–486molecular-targeted media, 482MRI

dynamic contrast-enhanced MRI and neovascularisation, 472–473

expansive remodeling, 467–468fibrous cap and lipid rich-necrotic core,

464–465fibrous cap disruption and platelet

aggregation, 465–466flow modeling, shear stress estimation,

469, 471IPH, 466–467long image acquisition times, 463severity of stenosis, 466superficial calcified nodules,

468–469three dimensional (3D)

data acquisition, 463USPIO-enhanced MRI and macrophage

content, 473–475nuclear imaging and ultrasonography,

481–483PET and SPECT, 475–477plaque characteristics,

459–460Nuclear imaging, 738

OOhara, T., 393Ohayon, J., 89, 112Oikawa, M., 26Okubo, M., 135Ombrellaro, M.P., 42Ophir, J., 767Optical imaging, 738, 740Overbeck, J.R., 889, 890

PPaigen, B., 636Papaharilaou, Y., 908, 909Partial differential equation (PDE), 162–163Partial least squares (PLS), 707, 712, 714Paterson, J.C., 391PCA. See Principal component analysisPDE. See Partial differential equationPDM. See Polyline distance metricPeak signal-to-noise ratio (PSNR), 168,

178–179Pearson’s R coefficient, 313Percent atheroma volume (PAV), 335, 337Performance evaluation and benchmarking

CA automated tracingCALEXia performances, 240–242CALEXia vs. ground truth tracings, 242PDM, 240

carotid wall segmentation and IMT measurement, 242–244

Perona, P., 162, 163PET imaging, 740–741Phage display technique, 726–727Phosphate buffered saline (PBS), 124Picker 1.5T Edge MRI scanner, 416Pignoli, P., 254, 291Plane wave excitation (PWE),

893–895Plaque

automated plaque analysis, 396–397calcification, 392–393carotid plaque enhancement, 394–395carotid plaque volume, 387–388eccentricity and remodelling, 393erosion, 15fibrous cap

arterial remodelling, 388automatic computer classifier

algorithm, 390contrast material gadolinium, 389fibrous connective tissue, 388juxtaluminal band, 389MDCTA, 390MRA, 389

hemorrhage and rupture, 379intraplaque haemorrhage, 391luminal narrowing, carotid

vulnerable plaqueacute myocardial infarction, 377atherosclerotic plaque, 378plaque classification, 378–379

smooth surface, 379surface irregularities, 379, 380thrombus, 391–392

940 Index

Plaque (cont.)types, analysis

ANOVA testing, 383carotid endarterectomy, 383cerebrovascular symptoms, 383, 386fatty, mixed and calcified plaques,

383–384hypercholesterolemia and

hyperfibrinogenemia, 386hypodense regions, 385–386lipid-lowering drug therapy, 386ROI, 384–385

ulcerationsatherosclerotic carotid plaque, 382CTA, 383definition, 379hypercholesterolemia, 380ischemic cerebral event, 380luminal stenosis, 381, 382MDCTA vs. US-ECD, 380–381

Plaque stress analysiscarotid plaque reconstructions

3D geometry reconstruction, 95–96MR imaging acquisition, 93plaque components segmentation,

93–953D structure-analysis vs. FSI, 91–922D vs. 3D structure, 90–91FEM, 90FSI simulation and boundary conditions

CCA, 97fluid flow parameters, 98

lipid core volume and fibrous cap thickness, 108–110

modeling procedure uncertainties analysisaxial stretch, 112geometry reconstruction reproducibility,

110–111material model definition, 111–112residual stress, 112

multiple patientsfluid domain results, 100plaque morphological impact,

103–106wall tensile stress, 100–102

rupture hypothesisde-bonding effect, 89local maximum stress, 88–89in vitro balloon angioplasty, 89

TIA patients, 106–108PLS. See Partial least squaresPolyline distance metric (PDM)

CA automated tracing, 240CALEXia performance, 244–245

carotid ultrasound images, intima-media thickness measurement, 311–312

performance metric design, 237–240Porsche, C., 383Positron emission tomography (PET),

475–477Prabhakaran, S., 462Principal component analysis (PCA), 706,

710–711, 714Protein microarray, 852Psaty, B.M., 609PSNR. See Peak signal-to-noise ratioPulsed wave (PW) Doppler, 883–886PWE. See Plane wave excitation

QQuantitative coronary angiography

(QCA), 577

RRadiograph, CEA, 5–6Raff, M.R., 365RAS. See Renin-angiotensin systemRayleigh and Rician probability density

function (PDF), 158Reactive oxygen species (ROS), 569Redgrave, J.N., 381, 388Regions of interest (ROIs)

local marking, 124–127systematic marking, 127

Renin-angiotensin system (RAS)antiatherosclerotic drugs

ACE2, 605angiotensinogen, 604–605AT1 and AT2, 605–606bradykinin, 606–607

hypertension and atherogenesis, 602–604

Ribbers, H., 775Rician distributions, 413RMSE. See Root mean squared errorRobert, R., 727Romero, J.M., 394Root mean squared error (RMSE), 168,

178–179Rossi, A.C., 223, 240, 288Ross, R., 26, 635Rothwell, P.M., 379, 392, 458, 534Roubin, S.G., 549Rudd, J.H., 372, 476, 477, 512Run-length method, 140Russell–Movat pentachrome, 126–127

941Index

SSaam, T., 197, 446, 447, 506Saba, L., 374, 376, 380, 382, 384, 389,

390, 394Sabetai, M.M., 461Salonen, J.T., 460Saloner, D., 910Sanz-Requena, R., 309SAPPHIRE, 44Savory, W.S., 373Scabia, M., 890Schroder, 3833-D Segmentation methods, 306–307Sethuraman, S.R., 790SFM. See Statistical feature matrixSGLDM. See Spatial gray level dependence

matricesShah, F., 502Shah, M., 181, 182, 184, 186, 296Shah, P.K., 113Sheikh, H.R., 169Shi, H., 778Signal-to-noise ratio (SNR), 168, 178–179Singh, N., 447, 508Single detector-row scanners, 355Single photon emission computed

tomography (SPECT), 475–477Sitzer, M., 502Snake-based segmentation strategy.

See Completely user-independent layers extraction algorithm based on signal analysis

SNAKES algorithm, 838, 839Solid domain parameter, 99Sorensen, H., 649Spagnoli, L.G., 19, 21, 465Spatial gray level dependence matrices

(SGLDM)distance measures, 171, 173feature extraction, 166univariate statistical analysis,

173, 176Speckle noise

CCA, 299Nakagami modeling, 302noise source, 289–290

Spectroscopic IVPA imagingcorrelation based approaches, 795first derivative, 792–793lesions composition, 793–794multi-wavelength, photoacoustic

response, 793optical absorption spectra, 792rabbit aorta samples, 794

Spence, J.D., 504SSIN. See Structural similarity indexStaessen, J.A., 610Stary, H.C., 440, 818Statistical feature matrix (SFM), 166,

177–178Statistical k-nearest-neighbour classifier

filtering method, 167filter performance investigation, 170texture analysis, 173, 177–178

Staub, D., 463Stein, J.H., 292, 294Stenosis severity, 21Sternocleidomastoid muscle (SCM), 539Stoica, R., 229Strain (shear) imaging, vulnerable plaques

detectionfatty streaks, 766intravascular strain imaging, 769–770non-invasive shear strain imaging

techniquesecho-tracking, 777–778radiofrequency-based ultrasound,

778–779relative lateral shift, 778

non-invasive strain imaging techniquescross-correlation-based methods,

773–774Doppler-based methods, 772registration-based method, 772–773ultrasound beam alignments, 771

schematic representation, plaques, 766transverse cross-sections

a-line based beam steering, 775image-based beam steering and

compounding, 775–777ultrasound strain imaging, 767–769

Structural similarity index (SSIN), 169, 178, 179

Suri, J.S., 221–248, 253–276, 281–316Sztajzel, R., 389

TTahara, N., 372, 477, 480Takaya, N., 18, 24, 25, 447, 459, 466, 508Tang, D., 89, 92Tawakol, A., 372, 476, 512TCD. See Transcranial DopplerTheron, J.G., 550Thin cap fibrous atheroma, 12–14Thitaikumar, A., 777Thorbjörnsdottir, P., 658TIA. See Transient ischemic attacks

942 Index

Time-of-flight magnetic resonance angiography (TOF-MRA), 411, 413

Tortoli, P., 285Total plaque area (TPA)

intensive statin treatment, 334–337intima media thickness (IMT),

326, 327scanning technique, 327

Total plaque volume (TPV)carotid atherosclerosis quantification,

331, 332IMT, 326, 327

Touboul, P.J., 292, 293Toussaint, J.F., 443Touze, E., 464Touzè, E., 40Trahey, G.E., 892, 893Transcranial Doppler (TCD), 502–504Transient ischemic attacks (TIA), 20, 39–41,

106–108, 530, 534Tree structure, 134, 135Triglyceride-rich lipoproteins

(TGRLP), 570Trivedi, R.A., 389, 464, 471, 474, 511Tunica adventitia, 73Tunica intima, 634, 635

UUdesen, J., 893U-King-Im, J.M., 471Ultrasonography (US)

CE US, 502conventional B-mode US

CCA, 499, 500echolucent plaques, 499GSM measurement, 499–500ICA, 499, 501pixel segmentation, 500standard B-mode US vs. compound US,

500, 501Ultrasound (US).

See also Ultrasonographyatheromasic disease, 60imaging, 736, 738, 740instrumental diagnosis, 65mechanical radiations, 55vs. MRI imaging, 56–57pharmacological therapies, effects, 65serial evaluations, 56

Ultrasound contrast agents, plaque characterization

advantage, 213

atherosclerotic process, 196CA ultrasound examination advantage,

198–199ceUS B-mode imaging

color-coded image, after analysis and tissue characterization, 205–207

CULEX segmentation, plaque, 204–205

CULEX vs. manual segmentation, 205–206

image after analysis and tissue characterization, 205–206

image enhancement, 204–205processing strategy, 203–204wall tissue enhancement, 203

ceUS plaque characterization and histology

plaque with calcium deposits, 207–208

soft unstable plaque, 209–211experimental protocol and

patients selectionGradenigo Hospital, 199–200testing protocol, 200–201

IMT risk indicator, advantage, 196–197

limitation, 212MATLAB implementation, 212techniques, 197–198ultrasound images segmentation strategy

CULEX segmentation, 202CULEX structure, 201

Ultrasound echo color Doppler (US-ECD)carotid artery imaging, 367–369vs. MDCTA, 380–381pathology and stroke risk, 377

Ultrasound strain imaging, 767–769Underhill, H.R., 468, 469, 507Universal quality index, 169, 178, 179Urbinati, S., 42US. See UltrasonographyUS-ECD. See Ultrasound echo color doppler

VVaccinia virus complement control protein

(VCP)complement inhibitor, 647diet-induced atherosclerosis model,

651–653myocardial damage, 658

van der Lugt, Aad, 387, 397van Der Meer, R.W., 910van der Wal, A.C., 9

943Index

Vascular disease and biologic NPsarterial calcification, 749biochemical characterization,

751–753FBS-derived NPs, 755Hill’s criteria, 756history, 750–751infection, 750Koch’s Postulates, 755–756microparticles, 757origin and life forms, 753–754

VCP. See Vaccinia virus complement control protein

Vector Dopplerblood velocity measurement, 889cross-beam Doppler, 888Doppler shift, 889–8902D vector Doppler, 891–892Overbeck’s system, 890vector velocity mapping, 890–891

Verdecchia, P., 611Very low-density lipoproteins (VLDL),

570–571Vessel wall segmentation

structure, 283–284ultrasound longitudinal B-Mode image,

284–285Vessel wall volume (VWV)

carotid atherosclerosis quantification, 331, 333, 334

3D and 2D carotid map generationatorvastatin and placebo

treatment, 339CCA and ICA, 340vessel wall and plaque thickness,

337, 338IMT, 327intensive statin treatment

ANOVA, 337atorvastatin and placebo treatment, 337carotid bifurcation, 335manual planimetry, 335PAV, 335, 337transverse and longitudinal 3D

ultrasound, 336, 337mapping spatial and

temporal changescarotid artery wall and lumen

segmentation, 340carotid stenosis, 341, 342flattened 2D thickness map, 341image segmentation, 341ISD, 340manual planimetry, 340

plaque and wall thickness changes, 343scan-rescan 2D thickness

difference maps, 343scanning technique, 327

Viator, J., 806Vibro-acoustography

arterial calcifications detectionarterial plaques detection, in vivo,

684–687contrast enhanced vibro-acoustography,

687–688excised human carotid arteries, 681–683normal arteries, in vivo imaging,

682–684clinical potential, 691detection sensitivity, 688–689exposure safety, 690image resolution, 681, 683, 689limitations, 690–691principle, 680–681quantitative measurements, 689–690ultrasound methods, 679

Vicenzini, E., 287Viergever, M.A., 297Virchow, R., 4, 25Virmani, R., 378VLDL. See Very low-density lipoproteinsVolume rendering (VR), 361–362Von Mises stress (VWTS), 99von Rokitansky, Carl, 4VR. See Volume renderingVulnerable plaque, 80VWTS. See Von Mises stress

WWagner, R.F., 154Waki, 389Wald, D.S., 46Walker, L.J., 381Wall shear stress (WSS), 879Wall tensile stress

fibrous cap, 100–101stress distributions, peak systole,

101, 104VWTS distribution, 100–101, 103

Wang, J.G., 613Warburton, E., 476Wardlaw, J.M., 377Ward, P.A., 648Warwick, R., 286Wasserman, B.A., 367, 395Watershed (WS) transform,

259–262

944 Index

Wendelhag, I., 295, 296Williams, D.J., 181–184, 186, 296Wilson, D.L., 413Wilson–Noble’s segmentation, 417, 420–423Wintermark, M., 390, 391, 397World Health Organization, 222, 282WSS. See Wall shear stress

YYang, J.-M., 806Yasuda, N., 605

Yonemura, A., 468, 589

Yuan, C., 446, 850

ZZahalka, A., 307Zhang, 290Zhao, S.Z., 92Zhao, X.Q., 19Zheng, J., 92