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MRI‐TRUS registration methodology for TRUS‐guided HDR prostate brachytherapy

Authors
  • McGeachy, Philip1, 2, 2
  • Watt, Elizabeth1, 2
  • Husain, Siraj2, 1
  • Martell, Kevin2, 1
  • Martinez, Pedro1, 2
  • Sawhney, Summit2
  • Thind, Kundan1, 2, 2
  • 1 Tom Baker Cancer Centre, Canada , (Canada)
  • 2 University of Calgary, Canada , (Canada)
Type
Published Article
Journal
Journal of Applied Clinical Medical Physics
Publisher
John Wiley and Sons Inc.
Publication Date
Jul 28, 2021
Volume
22
Issue
8
Pages
284–294
Identifiers
DOI: 10.1002/acm2.13292
PMID: 34318581
PMCID: PMC8364261
Source
PubMed Central
Keywords
Disciplines
  • Technical Notes
License
Unknown
External links

Abstract

Purpose High‐dose‐rate (HDR) prostate brachytherapy is an established technique for whole‐gland treatment. For transrectal ultrasound (TRUS)‐guided HDR prostate brachytherapy, image fusion with a magnetic resonance image (MRI) can be performed to make use of its soft‐tissue contrast. The MIM treatment planning system has recently introduced image registration specifically for HDR prostate brachytherapy and has incorporated a Predictive Fusion workflow, which allows clinicians to attempt to compensate for differences in patient positioning between imaging modalities. In this study, we investigate the accuracy of the MIM algorithms for MRI‐TRUS fusion, including the Predictive Fusion workflow. Materials and Methods A radiation oncologist contoured the prostate gland on both TRUS and MRI. Four registration methodologies to fuse the MRI and the TRUS images were considered: rigid registration (RR), contour‐based (CB) deformable registration, Predictive Fusion followed by RR (pfRR), and Predictive Fusion followed by CB deformable registration (pfCB). Registrations were compared using the mean distance to agreement and the Dice similarity coefficient for the prostate as contoured on TRUS and the registered MRI prostate contour. Results Twenty patients treated with HDR prostate brachytherapy at our center were included in this retrospective evaluation. For the cohort, mean distance to agreement was 2.1 ± 0.8 mm, 0.60 ± 0.08 mm, 2.0 ± 0.5 mm, and 0.59 ± 0.06 mm for RR, CB, pfRR, and pfCB, respectively. Dice similarity coefficients were 0.80 ± 0.05, 0.93 ± 0.02, 0.81 ± 0.03, and 0.93 ± 0.01 for RR, CB, pfRR, and pfCB, respectively. The inclusion of the Predictive Fusion workflow did not significantly improve the quality of the registration. Conclusions The CB deformable registration algorithm in the MIM treatment planning system yielded the best geometric registration indices. MIM offers a commercial platform allowing for easier access and integration into clinical departments with the potential to play an integral role in future focal therapy applications for prostate cancer.

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