Eric Poulin1, Karim Boudam2, Csaba Pinter3, Samuel Kadoury4, Andras Lasso3, Gabor Fichtinger3, Cynthia Ménard5. 1. Département de radio-oncologie et Axe Oncologie du Centre de recherche du CHU de Québec, CHU de Québec, Québec, Canada; Département de radio-oncologie, CHUM, Montréal, Québec, Canada. 2. Département de radio-oncologie, CHUM, Montréal, Québec, Canada. 3. Laboratory for Percutaneous Surgery, School of Computing, Queen's University, Kingston, Canada. 4. Department of Computer and Software Engineering, Polytechnique de Montréal, Montréal, Canada; CRCHUM, Université de Montréal, Montréal, Québec, Canada. 5. Département de radio-oncologie, CHUM, Montréal, Québec, Canada; CRCHUM, Université de Montréal, Montréal, Québec, Canada; TECHNA Institute, Toronto, Ontario, Canada. Electronic address: cynthia.menard@umontreal.ca.
Abstract
PURPOSE: The objective of this study was to develop and validate an open-source module for MRI to transrectal ultrasound (TRUS) registration to support tumor-targeted prostate brachytherapy. METHODS AND MATERIALS: In this study, 15 patients with prostate cancer lesions visible on multiparametric MRI were selected for the validation. T2-weighted images with 1-mm isotropic voxel size and diffusion weighted images were acquired on a 1.5T Siemens imager. Three-dimensional (3D) TRUS images with 0.5-mm slice thickness were acquired. The investigated registration module was incorporated in the open-source 3D Slicer platform, which can compute rigid and deformable transformations. An extension of 3D Slicer, SlicerRT, allows import of and export to DICOM-RT formats. For validation, similarity indices, prostate volumes, and centroid positions were determined in addition to registration errors for common 3D points identified by an experienced radiation oncologist. RESULTS: The average time to compute the registration was 35 ± 3 s. For the rigid and deformable registration, respectively, Dice similarity coefficients were 0.87 ± 0.05 and 0.93 ± 0.01 while the 95% Hausdorff distances were 4.2 ± 1.0 and 2.2 ± 0.3 mm. MRI volumes obtained after the rigid and deformable registration were not statistically different (p > 0.05) from reference TRUS volumes. For the rigid and deformable registration, respectively, 3D distance errors between reference and registered centroid positions were 2.1 ± 1.0 and 0.4 ± 0.1 mm while registration errors between common points were 3.5 ± 3.2 and 2.3 ± 1.1 mm. Deformable registration was found significantly better (p < 0.05) than rigid registration for all parameters. CONCLUSIONS: An open-source MRI to TRUS registration platform was validated for integration in the brachytherapy workflow.
PURPOSE: The objective of this study was to develop and validate an open-source module for MRI to transrectal ultrasound (TRUS) registration to support tumor-targeted prostate brachytherapy. METHODS AND MATERIALS: In this study, 15 patients with prostate cancer lesions visible on multiparametric MRI were selected for the validation. T2-weighted images with 1-mm isotropic voxel size and diffusion weighted images were acquired on a 1.5T Siemens imager. Three-dimensional (3D) TRUS images with 0.5-mm slice thickness were acquired. The investigated registration module was incorporated in the open-source 3D Slicer platform, which can compute rigid and deformable transformations. An extension of 3D Slicer, SlicerRT, allows import of and export to DICOM-RT formats. For validation, similarity indices, prostate volumes, and centroid positions were determined in addition to registration errors for common 3D points identified by an experienced radiation oncologist. RESULTS: The average time to compute the registration was 35 ± 3 s. For the rigid and deformable registration, respectively, Dice similarity coefficients were 0.87 ± 0.05 and 0.93 ± 0.01 while the 95% Hausdorff distances were 4.2 ± 1.0 and 2.2 ± 0.3 mm. MRI volumes obtained after the rigid and deformable registration were not statistically different (p > 0.05) from reference TRUS volumes. For the rigid and deformable registration, respectively, 3D distance errors between reference and registered centroid positions were 2.1 ± 1.0 and 0.4 ± 0.1 mm while registration errors between common points were 3.5 ± 3.2 and 2.3 ± 1.1 mm. Deformable registration was found significantly better (p < 0.05) than rigid registration for all parameters. CONCLUSIONS: An open-source MRI to TRUS registration platform was validated for integration in the brachytherapy workflow.
Authors: Jason S Lewis; Adam L Kesner; Lukas M Carter; Juan Camilo Ocampo Ramos; Wesley E Bolch Journal: Med Phys Date: 2021-03-09 Impact factor: 4.071
Authors: Christopher W Smith; Ryan Alfano; Douglas Hoover; Kathleen Surry; David D'Souza; Jonathan Thiessen; Irina Rachinsky; John Butler; Jose A Gomez; Mena Gaed; Madeleine Moussa; Joseph Chin; Stephen Pautler; Glenn S Bauman; Aaron D Ward Journal: Phys Imaging Radiat Oncol Date: 2021-07-30
Authors: Vincent Grégoire; Matthias Guckenberger; Karin Haustermans; Jan J W Lagendijk; Cynthia Ménard; Richard Pötter; Ben J Slotman; Kari Tanderup; Daniela Thorwarth; Marcel van Herk; Daniel Zips Journal: Mol Oncol Date: 2020-06-29 Impact factor: 6.603