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A Bayesian Nonrigid Registration Method to Enhance Intraoperative Target Definition in Image-guided Prostate Procedures through Uncertainty Characterization

1Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
2Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
American Institute of Physics
Publication Date:
Med Phys
Volume Number:
Issue Number:
Med Phys. 2012 Nov;39(11):6858-67.
PubMed ID:
deformable probabilistic registration, prostate, Brachytherapy
Appears in Collections:
NCIGT, Prostate Group, SLICER, SPL
R01 CA111288/CA/NCI NIH HHS/United States
P01 CA067165/CA/NCI NIH HHS/United States
P41 EB015898/EB/NIBIB NIH HHS/United States
P41 RR019703/RR/NCRR NIH HHS/United States
U01 CA151261/CA/NCI NIH HHS/United States
Generated Citation:
Pursley J., Risholm P., Fedorov A., Tuncali K., Fennessy F.M., Wells III W.M., Tempany C.M., Cormack R.A. A Bayesian Nonrigid Registration Method to Enhance Intraoperative Target Definition in Image-guided Prostate Procedures through Uncertainty Characterization. Med Phys. 2012 Nov;39(11):6858-67. PMID: 23127078. PMCID: PMC3494726.
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Purpose: This study introduces a probabilistic nonrigid registration method for use in image-guided prostate brachytherapy. Intraoperative imaging for prostate procedures, usually transrectal ultrasound (TRUS), is typically inferior to diagnostic-quality imaging of the pelvis such as endorectal magnetic resonance imaging (MRI). MR images contain superior detail of the prostate boundaries and provide substructure features not otherwise visible. Previous efforts to register diagnostic prostate images with the intraoperative coordinate system have been deterministic and did not offer a measure of the registration uncertainty. The authors developed a Bayesian registration method to estimate the posterior distribution on deformations and provide a case-specific measure of the associated registration uncertainty.Methods: The authors adapted a biomechanical-based probabilistic nonrigid method to register diagnostic to intraoperative images by aligning a physician's segmentations of the prostate in the two images. The posterior distribution was characterized with a Markov Chain Monte Carlo method; the maximum a posteriori deformation and the associated uncertainty were estimated from the collection of deformation samples drawn from the posterior distribution. The authors validated the registration method using a dataset created from ten patients with MRI-guided prostate biopsies who had both diagnostic and intraprocedural 3 Tesla MRI scans. The accuracy and precision of the estimated posterior distribution on deformations were evaluated from two predictive distance distributions: between the deformed central zone-peripheral zone (CZ-PZ) interface and the physician-labeled interface, and based on physician-defined landmarks. Geometric margins on the registration of the prostate's peripheral zone were determined from the posterior predictive distance to the CZ-PZ interface separately for the base, mid-gland, and apical regions of the prostate.Results: The authors observed variation in the shape and volume of the segmented prostate in diagnostic and intraprocedural images. The probabilistic method allowed us to convey registration results in terms of posterior distributions, with the dispersion providing a patient-specific estimate of the registration uncertainty. The median of the predictive distance distribution between the deformed prostate boundary and the segmented boundary was ≤3 mm (95th percentiles within ±4 mm) for all ten patients. The accuracy and precision of the internal deformation was evaluated by comparing the posterior predictive distance distribution for the CZ-PZ interface for each patient, with the median distance ranging from -0.6 to 2.4 mm. Posterior predictive distances between naturally occurring landmarks showed registration errors of ≤5 mm in any direction. The uncertainty was not a global measure, but instead was local and varied throughout the registration region. Registration uncertainties were largest in the apical region of the prostate.Conclusions: Using a Bayesian nonrigid registration method, the authors determined the posterior distribution on deformations between diagnostic and intraprocedural MR images and quantified the uncertainty in the registration results. The feasibility of this approach was tested and results were positive. The probabilistic framework allows us to evaluate both patient-specific and location-specific estimates of the uncertainty in the registration result. Although the framework was tested on MR-guided procedures, the preliminary results suggest that it may be applied to TRUS-guided procedures as well, where the addition of diagnostic MR information may have a larger impact on target definition and clinical guidance.

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