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Updated: Feb 15, 2026

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
Published on: April 9, 2019
Validation of MRI to TRUS registration for high-dose-rate prostate brachytherapy
Eric Poulin1, Karim Boudam2, Csaba Pinter3
1Département 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.
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.
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