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

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 treatm...

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Autores principales: McGeachy, Philip, Watt, Elizabeth, Husain, Siraj, Martell, Kevin, Martinez, Pedro, Sawhney, Summit, Thind, Kundan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364261/
https://www.ncbi.nlm.nih.gov/pubmed/34318581
http://dx.doi.org/10.1002/acm2.13292
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author McGeachy, Philip
Watt, Elizabeth
Husain, Siraj
Martell, Kevin
Martinez, Pedro
Sawhney, Summit
Thind, Kundan
author_facet McGeachy, Philip
Watt, Elizabeth
Husain, Siraj
Martell, Kevin
Martinez, Pedro
Sawhney, Summit
Thind, Kundan
author_sort McGeachy, Philip
collection PubMed
description 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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spelling pubmed-83642612021-08-23 MRI‐TRUS registration methodology for TRUS‐guided HDR prostate brachytherapy McGeachy, Philip Watt, Elizabeth Husain, Siraj Martell, Kevin Martinez, Pedro Sawhney, Summit Thind, Kundan J Appl Clin Med Phys Technical Notes 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. John Wiley and Sons Inc. 2021-07-28 /pmc/articles/PMC8364261/ /pubmed/34318581 http://dx.doi.org/10.1002/acm2.13292 Text en © 2021 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technical Notes
McGeachy, Philip
Watt, Elizabeth
Husain, Siraj
Martell, Kevin
Martinez, Pedro
Sawhney, Summit
Thind, Kundan
MRI‐TRUS registration methodology for TRUS‐guided HDR prostate brachytherapy
title MRI‐TRUS registration methodology for TRUS‐guided HDR prostate brachytherapy
title_full MRI‐TRUS registration methodology for TRUS‐guided HDR prostate brachytherapy
title_fullStr MRI‐TRUS registration methodology for TRUS‐guided HDR prostate brachytherapy
title_full_unstemmed MRI‐TRUS registration methodology for TRUS‐guided HDR prostate brachytherapy
title_short MRI‐TRUS registration methodology for TRUS‐guided HDR prostate brachytherapy
title_sort mri‐trus registration methodology for trus‐guided hdr prostate brachytherapy
topic Technical Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364261/
https://www.ncbi.nlm.nih.gov/pubmed/34318581
http://dx.doi.org/10.1002/acm2.13292
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