Validation of MRI to TRUS registration for high-dose-rate prostate brachytherapy.

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.

[1]  D. Bostwick,et al.  Understanding the pathological features of focality, grade and tumour volume of early‐stage prostate cancer as a foundation for parenchyma‐sparing prostate cancer therapies: active surveillance and focal targeted therapy , 2011, BJU international.

[2]  J. Pouliot,et al.  American Brachytherapy Society consensus guidelines for high-dose-rate prostate brachytherapy. , 2012, Brachytherapy.

[3]  C. Ménard,et al.  Clinical use of magnetic resonance imaging across the prostate brachytherapy workflow. , 2017, Brachytherapy.

[4]  C. Ménard,et al.  Use of magnetic resonance imaging in low-dose-rate and high-dose-rate prostate brachytherapy from diagnosis to treatment assessment: Defining the knowledge gaps, technical challenges, and barriers to implementation. , 2017, Brachytherapy.

[5]  M. Ranney,et al.  Beyond the bedside: Clinicians as guardians of public health, medicine and science , 2020, The American Journal of Emergency Medicine.

[6]  A. Zwinderman,et al.  Comparison of three radiotherapy modalities on biochemical control and overall survival for the treatment of prostate cancer: a systematic review. , 2009, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[7]  É. Vigneault,et al.  Image-guided high-dose-rate brachytherapy boost to the dominant intraprostatic lesion using multiparametric magnetic resonance imaging including spectroscopy: Results of a prospective study. , 2016, Brachytherapy.

[8]  M. Zelefsky,et al.  Magnetic resonance imaging-based salvage brachytherapy: Moving toward a focal paradigm. , 2017, Brachytherapy.

[9]  M. Peach,et al.  Systematic Review of Focal Prostate Brachytherapy and the Future Implementation of Image-Guided Prostate HDR Brachytherapy Using MR-Ultrasound Fusion , 2016, Prostate cancer.

[10]  Kemal Tuncali,et al.  Open-source image registration for MRI–TRUS fusion-guided prostate interventions , 2015, International Journal of Computer Assisted Radiology and Surgery.

[11]  B. Carey,et al.  Magnetic resonance imaging for the detection, localisation, and characterisation of prostate cancer: recommendations from a European consensus meeting. , 2011, European urology.

[12]  Milan Sonka,et al.  3D Slicer as an image computing platform for the Quantitative Imaging Network. , 2012, Magnetic resonance imaging.

[13]  Dean C. Barratt,et al.  Fully automated prostate magnetic resonance imaging and transrectal ultrasound fusion via a probabilistic registration metric , 2013, Medical Imaging.

[14]  Jocelyne Troccaz,et al.  MRI/TRUS data fusion for prostate brachytherapy. Preliminary results , 2004, Medical physics.

[15]  A. Polo,et al.  External beam radiotherapy plus single-fraction high dose rate brachytherapy in the treatment of locally advanced prostate cancer. , 2014, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[16]  Baris Turkbey,et al.  Current status of magnetic resonance imaging (MRI) and ultrasonography fusion software platforms for guidance of prostate biopsies , 2014, BJU international.

[17]  F. Hausdorff.  Grundzüge der Mengenlehre , 1914 .

[18]  C. Ménard,et al.  Boosting imaging defined dominant prostatic tumors: a systematic review. , 2013, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[19]  P. McLaughlin,et al.  Prostate magnetic resonance imaging for brachytherapists: Diagnosis, imaging pitfalls, and post-therapy assessment. , 2016, Brachytherapy.

[20]  Eli Konen,et al.  Deformable registration of trans-rectal ultrasound (TRUS) and magnetic resonance imaging (MRI) for focal prostate brachytherapy , 2016, International Journal of Computer Assisted Radiology and Surgery.

[21]  Lawrence H. Staib,et al.  Learning Non‐rigid Deformations for Robust, Constrained Point‐based Registration in Image‐Guided MR‐TRUS Prostate Intervention , 2017, Medical Image Anal..

[22]  Andras Lasso,et al.  SlicerRT: radiation therapy research toolkit for 3D Slicer. , 2012, Medical physics.

[23]  G. Viani,et al.  Higher-than-conventional radiation doses in localized prostate cancer treatment: a meta-analysis of randomized, controlled trials. , 2009, International journal of radiation oncology, biology, physics.

[24]  David A Jaffray,et al.  Accuracy and sensitivity of finite element model-based deformable registration of the prostate. , 2008, Medical physics.

[25]  Jurgen J Fütterer,et al.  Accuracy of multiparametric MRI for prostate cancer detection: a meta-analysis. , 2014, AJR. American journal of roentgenology.

[26]  H. Hricak,et al.  Clinically significant prostate cancer local recurrence after radiation therapy occurs at the site of primary tumor: magnetic resonance imaging and step-section pathology evidence. , 2007, International journal of radiation oncology, biology, physics.

[27]  B. Carey,et al.  Multi-parametric MRI-guided focal tumor boost using HDR prostate brachytherapy: a feasibility study. , 2014, Brachytherapy.

[28]  William M. Wells,et al.  Two solutions for registration of ultrasound to MRI for image-guided prostate interventions , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[29]  M. Milette,et al.  Ultrasound-planned high-dose-rate prostate brachytherapy: dose painting to the dominant intraprostatic lesion. , 2014, Brachytherapy.

[30]  Zeike A. Taylor,et al.  MR to ultrasound registration for image-guided prostate interventions , 2012, Medical Image Anal..

[31]  K. Brock,et al.  Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132 , 2017, Medical physics.

[32]  Hans Johnson,et al.  BRAINSFit: Mutual Information Registrations of Whole-Brain 3D Images, Using the Insight Toolkit , 2007, The Insight Journal.

[33]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[34]  Stephan E Maier,et al.  Multiparametric MRI of prostate cancer: An update on state‐of‐the‐art techniques and their performance in detecting and localizing prostate cancer , 2013, Journal of magnetic resonance imaging : JMRI.