a 3d u ltrasound-based t racking s ystem for p rostate b iopsy d istribution q uality i nsurance and...
TRANSCRIPT
A 3D ULTRASOUND-BASED TRACKING SYSTEM FOR PROSTATE BIOPSY DISTRIBUTION QUALITY
INSURANCE AND GUIDANCE.
PhD Thesis
Michael Baumann
Supervisors
Jocelyne TroccazVincent Daanen
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Context of this thesis
TIMC laboratory• specializing in computer-assisted medical interventions for more than
twenty years now• many clinical and industrial collaborations
Pitié-Salpétrière hospital, urology department• active support of this work and very inspiring exchanges• clinical data acquisition on more than 70 patients now
Koelis SA• industrial partner• objective: commercialize products based on prostate tracking
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Introduction
Prostate
Prostate Cancer• most frequent cancer in men
- ~220.000 new cases in US (2007)- ~345.000 new cases in EU25
(2006)• second cause of cancer death for men
- 27.000 deaths in US (2007)- 87.400 deaths in EU25 (2006)
• slow growing disease• affects mostly elder men (>50 years)
Bladder
Seminal Vesicles
Rectum
Prostate
Urethra
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Introduction
Prostate Specific Antigene (PSA) screening• biological tumor marker• sensitivity for 4ng/ml threshold: 68-83% (clinically significant
cancer)• specificity: ~30% false positives!
Digital Rectal Exams (DRE)• highly varying sensitivity in clinical studies reported: 18% to
68%• specificity: 4% to 33%• complementary to PSA screening
Prostate Biopsies• Sensitivity: 60-80 % (clinically significant cancer)• Specificity: >95% (histological analysis)• invasive programmed only if DRE/PSA positive• dilemma: false negatives repeated biopsies
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Prostate Biopsies• 2D transrectal ultrasound (TRUS) control• needle guide on probe
• guide aligned with longitudinal plane of probe
2D TRUS probe with needle guide
Introduction
longitudinal cut
corresponding 2D US image
with needle trajectory
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Introduction
Biopsy targets• prostate cancer is isoechogenic
systematic targets• McNeal’s 3-zone model: central zone (CZ),
transition zone (TZ), peripheral zone (PZ)• 68% of cancer can be found in peripheral
zone
Systematic 12-core protocol
• clinical representation in (pseudo-)coronal plane
coronal plane
coronal plane
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Prostate Motion Problem
Prostate motion• main challenge for any prostate tissue tracking system• displacements and deformations
Transrectal biopsy specific: probe-related motion• end-fire probe• deformations and displacements due to probe pressure
Neighboring organs (diaphragm motion, rectal and bladder filling)• minor impact during prostate biopsies
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Prostate Motion Problem
Patient motion• (small) deformations
• displacements with respect to surrounding tissues
• displacements with respect to operating room (pelvis movements!)
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Introduction
Biopsy and Target Localization Problem• only rudimentary knowledge about biopsy
position• at all stages of intervention!
Pre-interventional stage/planning• n-core protocol target definition highly
approximate• targets have to be mentally mapped into patient
anatomy
During intervention: target localization problem• difficult to aim invisible target under 2D control
- ultrasound: few structural information- 2D: no depth information- prostate motion
finding the target : what do we aim exactly?
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Introduction
Target localization problem (ctd)• there exist better targets than systematic protocol• high quality cancer distribution atlas available [Shen’01]
- simulations: biopsy sensitivity > 96% with only 6 needles (transperineal access)
• suspicious lesions identified on IRM• repeated biopsy series
- avoid already sampled tissues (negative targets)
• how to aim these targets?
After intervention : sample localization problem• where were the samples taken exactly?• quality control?
- are there unsampled regions?• difficult to map histological cancer information back to anatomy
difficult to use histological information for focal treatment planning
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Magnet Resonance imaging-based approaches• objective : target suspicious lesions detected on MR images• biopsy under MRI control• instruments calibrated with MR frame
Beyersdorff [05], Musil, Krieger et al. [04,05,07], Stoianovici [07]• IRM compatible biopsy acquisition instruments/robot
• pro: possibility to aim IRM targets• con: cannot detect/compensate patient movements
- would require high resolution, real-time MRI• con: diagnosis: cost-benefit ratio unsatisfying
- several millions of biopsies/year in US and EU
Existing Solutions
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Probe tracking + registration based approaches• 2D transrectal ultrasound• track US probe with optical or magnetic tracking system
- identifies view cone motion• register 2D tracking images with free-hand reference volume
- identifies prostate motion
Xu et al. [07]: Magnetic probe tracking + registration• pro: can compensate smaller rigid prostate-movements• con: free-hand volume with end-fire probe low accuracy• con: rigid registration• con: registration of lateral biopsy images not robust (partial
gland problem)• con: difficult to compensate large pelvis movements
Existing Solutions
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Outline of the Presentation
Prostate Tissue Tracking and Guidance• clinical and scientific objectives• soft-tissue tracking
Prostate Image Registration• registration framework• multi-resolution techniques• image distance metric (rigid)• probe movement model• rigid refinement• elastic registration framework• forces for elastic registration
Experiments and Results• registration success rate• accuracy• biopsy maps and targeting accuracy study
Discussion Conclusion and Potential Applications
• clinical and scientific contributions• potential applications
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Objectives
Scientific objectives• prostate tissue tracking
- establish tissue correspondence- with respect to a reference space
• goal: establish correspondence between- biopsy site planning- reference space- needle position during intervention
Clinical objectives• more sophisticated targets
- MRI, statistical cancer atlas, unsampled zones when repeating biopsies
• guide clinician to target• feed-back to clinician about exact sample position
- immediately and after intervention• biopsy maps
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guidance:
target projection into tracking volume
anchor volume:
acquired before intervention
defines the reference space
tracking volume:
acquired during intervention:
“contains” sample trajectory
needle projection:
projection into anchor volume
projection can lead to curbed trajectories
registration:
establishment of correspondences for identical tissues present in both images
Image-based Prostate Tracking Framework
Proposed Solution• 3D ultrasound-based• hybrid registration
- image-based- a priori model based
• deformation estimation• no probe tracking• miniminal overhead for clinician, no segmentation
3D ultrasoundview cone
anchor volume tracking volume
biopsy map:
contains projections of all samples
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Registration Framework
Image Registration• Optimization (minimization) problem
• φ = transformation model• T = template/transformed image (R3 R)• R = reference/fixed image
• D[.] = cost functional
Problems• registration only efficient with local minimization (downhill
search)• successful local minimization requires
- locally unimodal cost functional- start point inside the convex region
• the more degrees of freedom (DOF) of φ, the more difficult to find unimodal region of D[R,T,φ] !
OK
KO KO
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3 DOF 6 DOF ~ 125.000 DOF
voxel intensity based image distance metrics
a priori models
multi-resolution approaches
optimization techniques
Proposed approach
Registration Framework
Probe kinematics based rigid presearch
Refinement of rigid estimate Elastic
estimation
multivariate correlation coefficient
parametric systematic
search
SSD with local intensity shift
loss-containing multi-resolution techniques
endorectal probe
kinematics
bio-mechanical probe insertion
parametric local
optimization
variational optimization
inverse consistency
linear elasticity
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Multi-Resolution
Multi-resolution approach• Gaussian pyramid• registration performed on different resolution levels
Coarse resolutions and information loss
probe kinematics
rigid refinement
elastic
?
70% 56%
≤ 50% of fine-grid voxel mask coarse-grid voxelelse use average of available voxel
attention: introduces, however, local information shifts
US-specific: complex image masks
problematic when computing level n+1 from level n
level n level n+1
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70% 71%
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Multi-resolution
50-percent rule• use it for pyramid construction
• for interpolation
• for every other computation on multiple voxels- gradient computation (image distance metrics!)- Gaussian smoothing
Conclusion• Makes high-speed volume to volume registration possible
- reliable registration on very coarse levels• Disadvantage
- introduces small local information shifts
probe kinematics
rigid refinement
elastic
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50% rule
level 5
standard
level 5
standard
level 5
50% rule
level 5
level 1
level 1
2020
Distance metric (rigid)
Image distance metric (Rigid Registration)• correlation coefficient (CC) based
- well-proven for monomodal registration• multivariate application
- intensity image + gradient magnitude image- more robust results on coarse levels
probe kinematics
rigid refinement
elastic
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raw image gradient magnitude
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Challenge• probe used to guide needle view cone motion• adds up to prostate motion
• motion too large for capture range of image distance metric- direct downhill/local registration only ~30-40% success rate
Observations• probe head always in contact with rectal wall in front of prostate
- if not, no prostate image or needle trajectory outside prostate
• anal sphincter heavily constrains probe motion- fix point for probe motion
• most important rotations occur around probe axis (when switching lobe)
Probe kinematics
0°-30° 180°
0° 180 360°
OK
KO KO
probe kinematics
rigid refinement
elastic
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Probe kinematics
Model of endorectal probe kinematics:
• approximate prostate capsule with ellipsoid from bounding box• estimate rectal probe fix point• admit only positions for which
- the probe axis lies on the fix point- the probe origin lies on the membrane
• 3 degrees of freedom only- can be exhaustively explored in reasonable time!
Advantages• Makes solution independent of external tracking system!• Solves patient motion problem!
PrSurf(0,0)
CPro
Bounding Box
Probe position in reference image
FPRect
Bounding Box
Prostate Ellipsoid
β PrSurf(α,β)
OUS
Bounding Box
Prostate Ellipsoid
λ
probe kinematics
rigid refinement
elastic
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Rigid Refinement
Refinement of rigid estimate
rigid registration of 5 best transformations
provided by probe kinematics
high quality registration of best result
probe kinematics
rigid refinement
elastic
high quality local search:
from coarse to fine
high speed:
optimize on coarsest level
classical local/downhill search algorithm:
Powell-Brent
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Elastic Registration
Prostate deformations• relatively small (several millimeters)• strongest near probe head• difficult to estimate:
- few image information near probe head
Transformation model: displacement field
Framework
• : linear elastic potential– regularizes/smoothes displacement field– minimal when no deformation strong regularizer
• : SSD variant to measure image distance• : bio-mechanical simulation of probe insertion• : inverse consistency constraints
probe kinematics
rigid refinement
elastic
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Elastic Registration
Elastic regularization: solution scheme• variational approach• necessary condition for solver u* of cost function:
- Gâteaux derivative at u* vanishes for all perturbations ψ• Euler-Lagrange equations for linear elastic regularization:
• trick: separate force computation and regularization1. accumulate forces2. solve Euler-Lagrange equations
• then we get an elliptic boundary value problem of the form
• trick: introduce artificial time to obtain iterative gradient descent scheme
probe kinematics
rigid refinement
elastic
gradient of linear elastic potential
gradients of distance metrics
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Force terms
Image based forces• correlation coefficient: statistically not robust when locally
computed• SSD
• assumes identity between R and Tû
- does not correspond to reality!- changes in ultrasound gain, probe pressure and ultrasound
direction
Local intensity shift model• additive model:• b estimated with Gaussian convolutions on R and T
• resulting force term
probe kinematics
rigid refinement
elastic
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Force terms
Bio-mechanical probe insertion model• model of probe-related tissue displacements
• Interpret displacement differences as forces in the estimation process
probe kinematics
rigid refinement
elastic
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Force terms
Inverse consistency forces• Observation: forward and backward estimation u and v not
symmetric:
• Zhang’s approach [’06]- estimate u and v simultaneously- enforce inverse consistency by minimizing- alternating optimization process:
• resulting force term
u
v
probe kinematics
rigid refinement
elastic
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Proposed approach
Registration Framework
Probe kinematics based rigid presearch
Refinement of rigid estimate Elastic
estimation
multivariate correlation coefficient
parametric systematic
search
SSD with local intensity shift
loss-containing multi-resolution techniques
endorectal probe
kinematics
bio-mechanical probe insertion
parametric local
optimization
variational optimization
inverse consistency
linear elasticity
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Experiments and results
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Experiments and Results
Experiments• on real patient data• Pitie-Salpétrière Hospital, Paris, urology department
- P. Mozer, G. Chevreau, S. Bart, J.-C. Bousquet• 3D ultrasound images (GE Voluson, RIC5-9 probe)
- acquired before biopsies and after each sample acquisition- targeting carried out under 2D US control
Registration example
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Experiments and Results
Rigid Registration • Algorithm tested on 785 image pairs from 47 patients• 27 mis-registrations (success-rate 96.5 %)
Conclusion• probe movement model works fine!
ultrasound depth
ultrasound quality
partial contact
PrSurf(0,0)
CPro
Bounding Box
Probe position in reference image
FPRect
Bounding Box
Prostate Ellipsoid
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OUS
Bounding Box
Prostate Ellipsoid
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Experiments and Results
Accuracy study• 208 registrations on data from 14 patients• manual point fiducial segmentation (calcifications, dark spots)• error computed on Euclidean distances of corresponding
fiducials
Registration accuracy
• rigid optimization performed on resolution levels 5 to 3• elastic optimization performed on resolution levels 6 to 3
Conclusion• accuracy sufficient for many clinical applications
rigid elasticfiducial distances (RMS) 1,41 mm 1,10 mmfiducial distances (max) 3,84 mm 2,93 mmexecution time 6,5 s 16,7 s
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Experiments and Results
First Application: Biopsy maps• show targeting difficulties
P. Mozer, M. Baumann, G. Chevreau, A. Moreau-Gaudry [Mozer’08]• apex and base targets more difficult to reach
than central gland• operator learning curve measured
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Discussion
Automatic registration validation• visual validation time-consuming and operator-dependent• open issue: automatic detection of failures!• necessary for guidance!
Registration and real-time• requires 5 – 15 seconds• stream parallelization:
- algorithm mainly consists of image convolutions- can be parallelized on a voxel per voxel basis- well suited for latest graphic card architectures (stream
processors)• registration times of 1 second or less should be feasible
Similarity measures• Good performance for intra-series registration• Still to be evaluated for inter-series registration
- only one patient with two biopsy series for instance• Intensity shift model
- depends strongly on parameter σ of Gaussian convolution
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Probe movement model (rigid registration)• very good success rate
- no probe tracking necessary less hardware in OR! Simpler workflow and logistics!
• improvements with model to data fitting possible• should further improve success rate
Bio-mechanical probe insertion model (elastic registration)• for about 50% image pairs, the model improves elastic
registration• but: sometimes inadequate model of reality
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Discussion
Clinical acceptability• only slight modification of classical acquisition protocol
- bounding box placement- registration validation (probably post-op step)
• no additional instruments/hardware in operation room• cost effective: cost similar to current procedure
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Scientific contributions• probe movement model
- robust- no probe tracking hardware required- completely solves patient movement problem- reusable for many endocavitary US interventions!
• loss-containing multi-resolution filtering and interpolation- robust optimization on very sparse resolution levels
• hybrid model- and image-based elastic deformation framework• novel voxel similarity measure for elastic registration
- remarkably robust- simple
• proof of concept on large set of patient data
Medical contributions• biopsy accuracy study on biopsy maps
- more difficult to reach apex/base than mid-gland• operator learning curve proven
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Future work/Prospects
Potential Applications : Biopsies• biopsy maps
- immediate feed-back, post-interventional quality control• cancer maps
- map histological results on 3D biopsy map• guidance
- assist clinician during targeting- requires automatic registration validation and real-time
registration• guidance MRI target mapping
- reach MRI targets under ultrasound control- requires
– MRI to ultrasound registration• guidance repeated biopsy series
- avoid multiple sampling- visualize already sampled tissues
• guidance cancer atlas targets- define targets with cancer probability atlas (Shen’01)- map them onto anchor volume
– requires atlas to ultrasound volume registration
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Future work/Prospects
Potential applications : Therapy• improve accuracy of ultrasound-guided therapy
- brachytherapy, HIFU, cryotherapy, …• focal therapy?
- currently: two unknowns after positive biopsy findings1. shape of the tumor2. exact location of the biopsy
- not accurate enough for focal therapy- we solve 2!- sufficient for focal therapy?
– in combination with statistical tumor atlas?
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Publications and References
Publications [Baumann’07] M. Baumann, P. Mozer, V. Daanen, J. Troccaz. Towards 3D
Ultrasound Image Based Soft Tissue Tracking: a Transrectal Ultrasound Prostate Image Alignment System. MICCAI'07, Brisbane, Australia, 2007. Springer LNCS 4792.
[Mozer’07] P. Mozer, M.Baumann, G. Chevreau, J. Troccaz. “Fusion d’images : application au contrôle de la distribution des biopsies prostatiques,” Progrès en Urologie (les Cahiers de la Formation Continue), vol. 18 (1), 2008
[Baumann’08] M. Baumann, P. Mozer, V. Daanen, J. Troccaz. “Fast and robust elastic registration of endorectal 3D ultrasound prostate volumes for transrectal prostate needle puncture tracking,” In proceedings of CARS’08, Barcelona, 2008
References [Shen’04] D. Shen, Z. Lao, J. Zeng, W. Zhang, I. A. Sesterhenn, L. Sun, J. W.
Moul, E. H. Herskovits, G. Fichtinger, and C. Davatzikos. “Optimization of biopsy strategy by a statistical atlas of prostate cancer distribution,” Medical Image Analysis, vol. 8, no. 2, pp. 139–150, 2004.
[Zhang’05] Z. Zhang, Y. Jiang, and H. Tsui. “Consistent multi-modal non-rigid registration based on a variational approach,” Pattern Recognition Letters, pp. 715–725, 2006.
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Acknowledgements
Urology department Pitié-Salpétrière• Pierre Mozer, Grégoire Chevreau, Stéphane Bart
Koelis SA• Antoine Leroy • Vincent Daanen
TIMC• GMCAO group• Jocelyne Troccaz
and everyone else who supported this project during the last three years!
Funding: • 2004-06: ”Programme Hospitalier de Recherche Clinique -
Prostate-Echo”, French ministry of research• 2005-07: “Surgétique Minimalement Invasive (SMI)”, Agence
Nationale de Recherche (ANR)• 2005-08: Association Nationale de la Recherche Technique,
bourse CIFRE
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Inadequate probe model• possible explanation
ultrasound gel no elastic
deformation!!
gland not deformed at
all
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Separate elastic estimation• first step:
- estimate deformations caused by probe forces• second step:
- estimate deformations caused by image forces- start optimization with probe deformation as initial guess
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Elastic Regularization
Elastic regularization: solution scheme (ctd)• von Neumann stability analysis of numerical scheme yields
Stability criterion and elasticity parameters• forces in our framework are not physical
- derived from distances- how to calibrate them with the elastic forces?
• Young’s modulus E has no physical meaning- interpret it as free parameter- control elasticity parameters with Poisson’s coefficient v
and ∆t
• seek best balance between smoothness and convergence rate balance elastic smoothing and maximally admitted deformation
probe kinematics
rigid refinement
elastic
Young’s modulus
Poisson’s coefficient
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Elastic Registration
Elastic regularization: solution scheme (ctd)• solved with Gauss-Seidel and full multigrid strategy
Boundary conditions• bending side-walls, fixed edges• good model for probe insertion
probe kinematics
rigid refinement
elastic
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Biopsy acquisition• patient in dorsal or lateral
position• local anesthesia• 12 acquisitions
Introduction