{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T14:39:03Z","timestamp":1773671943802,"version":"3.50.1"},"reference-count":32,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2012,10,22]],"date-time":"2012-10-22T00:00:00Z","timestamp":1350864000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01CA151261"],"award-info":[{"award-number":["U01CA151261"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P01\u2010CA67165"],"award-info":[{"award-number":["P01\u2010CA67165"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1R01CA111288\u201001A1"],"award-info":[{"award-number":["1R01CA111288\u201001A1"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P41RR019703"],"award-info":[{"award-number":["P41RR019703"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P41EB015898"],"award-info":[{"award-number":["P41EB015898"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2012,11]]},"abstract":"<jats:sec><jats:title>Purpose:<\/jats:title><jats:p>This study introduces a probabilistic nonrigid registration method for use in image\u2010guided prostate brachytherapy. Intraoperative imaging for prostate procedures, usually transrectal ultrasound (TRUS), is typically inferior to diagnostic\u2010quality 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\u2010specific measure of the associated registration uncertainty.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods:<\/jats:title><jats:p>The authors adapted a biomechanical\u2010based probabilistic nonrigid method to register diagnostic to intraoperative images by aligning a physician\u02c8s segmentations of the prostate in the two images. The posterior distribution was characterized with a Markov Chain Monte Carlo method; the maximum<jats:italic>a posteriori<\/jats:italic> 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\u2010guided 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\u2010peripheral zone (CZ\u2010PZ) interface and the physician\u2010labeled interface, and based on physician\u2010defined landmarks. Geometric margins on the registration of the prostate\u02c8s peripheral zone were determined from the posterior predictive distance to the CZ\u2010PZ interface separately for the base, mid\u2010gland, and apical regions of the prostate.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results:<\/jats:title><jats:p>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\u2010specific estimate of the registration uncertainty. The median of the predictive distance distribution between the deformed prostate boundary and the segmented boundary was \u2a7d3 mm (95th percentiles within \u00b14 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\u2010PZ interface for each patient, with the median distance ranging from \u22120.6 to 2.4 mm. Posterior predictive distances between naturally occurring landmarks showed registration errors of \u2a7d5 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions:<\/jats:title><jats:p>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\u2010specific and location\u2010specific estimates of the uncertainty in the registration result. Although the framework was tested on MR\u2010guided procedures, the preliminary results suggest that it may be applied to TRUS\u2010guided procedures as well, where the addition of diagnostic MR information may have a larger impact on target definition and clinical guidance.<\/jats:p><\/jats:sec>","DOI":"10.1118\/1.4760992","type":"journal-article","created":{"date-parts":[[2012,10,22]],"date-time":"2012-10-22T19:05:45Z","timestamp":1350932745000},"page":"6858-6867","source":"Crossref","is-referenced-by-count":10,"title":["A Bayesian nonrigid registration method to enhance intraoperative target definition in image\u2010guided prostate procedures through uncertainty characterization"],"prefix":"10.1002","volume":"39","author":[{"given":"Jennifer","family":"Pursley","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Petter","family":"Risholm","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andriy","family":"Fedorov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kemal","family":"Tuncali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fiona M.","family":"Fennessy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"suffix":"III","given":"William M.","family":"Wells","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Clare M.","family":"Tempany","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert A.","family":"Cormack","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2012,10,22]]},"reference":[{"key":"e_1_2_7_2_1","volume-title":"Cancer Facts and Figures 2011","author":"American Cancer Society","year":"2011"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.juro.2007.01.134"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2009.1348"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0360\u20103016(98)00271\u20105"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0090\u20104295(03)00248\u20106"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1076\u20106332(03)80460\u20109"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/50\/12\/011"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2004.07.677"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.3109\/10929080209146034"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/51\/17\/015"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2005.02.001"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/50\/24\/008"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/50\/12\/010"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.21955"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1414009"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1915012"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2007.01.056"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2965263"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2011.5872467"},{"key":"e_1_2_7_21_1","volume-title":"19th Annual Meeting of the ISMRM","author":"Fedorov A.","year":"2011"},{"key":"e_1_2_7_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.23688"},{"key":"e_1_2_7_23_1","first-page":"698","article-title":"The NA\u2010MIC Kit: ITK, VTK, pipelines, grids and 3D slicer as an open platform for the medical image computing community","author":"Pieper S.","year":"2006","journal-title":"Proc. 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