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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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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. |
format | Online Article Text |
id | pubmed-8364261 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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