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Prostatic urinary tract visualization with super-resolution deep learning models
In urethra-sparing radiation therapy, prostatic urinary tract visualization is important in decreasing the urinary side effect. A methodology has been developed to visualize the prostatic urinary tract using post-urination magnetic resonance imaging (PU-MRI) without a urethral catheter. This study i...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9821403/ https://www.ncbi.nlm.nih.gov/pubmed/36607999 http://dx.doi.org/10.1371/journal.pone.0280076 |
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author | Yoshimura, Takaaki Nishioka, Kentaro Hashimoto, Takayuki Mori, Takashi Kogame, Shoki Seki, Kazuya Sugimori, Hiroyuki Yamashina, Hiroko Nomura, Yusuke Kato, Fumi Kudo, Kohsuke Shimizu, Shinichi Aoyama, Hidefumi |
author_facet | Yoshimura, Takaaki Nishioka, Kentaro Hashimoto, Takayuki Mori, Takashi Kogame, Shoki Seki, Kazuya Sugimori, Hiroyuki Yamashina, Hiroko Nomura, Yusuke Kato, Fumi Kudo, Kohsuke Shimizu, Shinichi Aoyama, Hidefumi |
author_sort | Yoshimura, Takaaki |
collection | PubMed |
description | In urethra-sparing radiation therapy, prostatic urinary tract visualization is important in decreasing the urinary side effect. A methodology has been developed to visualize the prostatic urinary tract using post-urination magnetic resonance imaging (PU-MRI) without a urethral catheter. This study investigated whether the combination of PU-MRI and super-resolution (SR) deep learning models improves the visibility of the prostatic urinary tract. We enrolled 30 patients who had previously undergone real-time-image-gated spot scanning proton therapy by insertion of fiducial markers. PU-MRI was performed using a non-contrast high-resolution two-dimensional T2-weighted turbo spin-echo imaging sequence. Four different SR deep learning models were used: the enhanced deep SR network (EDSR), widely activated SR network (WDSR), SR generative adversarial network (SRGAN), and residual dense network (RDN). The complex wavelet structural similarity index measure (CW-SSIM) was used to quantitatively assess the performance of the proposed SR images compared to PU-MRI. Two radiation oncologists used a 1-to-5 scale to subjectively evaluate the visibility of the prostatic urinary tract. Cohen’s weighted kappa (k) was used as a measure of agreement of inter-operator reliability. The mean CW-SSIM in EDSR, WDSR, SRGAN, and RDN was 99.86%, 99.89%, 99.30%, and 99.67%, respectively. The mean prostatic urinary tract visibility scores of the radiation oncologists were 3.70 and 3.53 for PU-MRI (k = 0.93), 3.67 and 2.70 for EDSR (k = 0.89), 3.70 and 2.73 for WDSR (k = 0.88), 3.67 and 2.73 for SRGAN (k = 0.88), and 4.37 and 3.73 for RDN (k = 0.93), respectively. The results suggest that SR images using RDN are similar to the original images, and the SR deep learning models subjectively improve the visibility of the prostatic urinary tract. |
format | Online Article Text |
id | pubmed-9821403 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-98214032023-01-07 Prostatic urinary tract visualization with super-resolution deep learning models Yoshimura, Takaaki Nishioka, Kentaro Hashimoto, Takayuki Mori, Takashi Kogame, Shoki Seki, Kazuya Sugimori, Hiroyuki Yamashina, Hiroko Nomura, Yusuke Kato, Fumi Kudo, Kohsuke Shimizu, Shinichi Aoyama, Hidefumi PLoS One Research Article In urethra-sparing radiation therapy, prostatic urinary tract visualization is important in decreasing the urinary side effect. A methodology has been developed to visualize the prostatic urinary tract using post-urination magnetic resonance imaging (PU-MRI) without a urethral catheter. This study investigated whether the combination of PU-MRI and super-resolution (SR) deep learning models improves the visibility of the prostatic urinary tract. We enrolled 30 patients who had previously undergone real-time-image-gated spot scanning proton therapy by insertion of fiducial markers. PU-MRI was performed using a non-contrast high-resolution two-dimensional T2-weighted turbo spin-echo imaging sequence. Four different SR deep learning models were used: the enhanced deep SR network (EDSR), widely activated SR network (WDSR), SR generative adversarial network (SRGAN), and residual dense network (RDN). The complex wavelet structural similarity index measure (CW-SSIM) was used to quantitatively assess the performance of the proposed SR images compared to PU-MRI. Two radiation oncologists used a 1-to-5 scale to subjectively evaluate the visibility of the prostatic urinary tract. Cohen’s weighted kappa (k) was used as a measure of agreement of inter-operator reliability. The mean CW-SSIM in EDSR, WDSR, SRGAN, and RDN was 99.86%, 99.89%, 99.30%, and 99.67%, respectively. The mean prostatic urinary tract visibility scores of the radiation oncologists were 3.70 and 3.53 for PU-MRI (k = 0.93), 3.67 and 2.70 for EDSR (k = 0.89), 3.70 and 2.73 for WDSR (k = 0.88), 3.67 and 2.73 for SRGAN (k = 0.88), and 4.37 and 3.73 for RDN (k = 0.93), respectively. The results suggest that SR images using RDN are similar to the original images, and the SR deep learning models subjectively improve the visibility of the prostatic urinary tract. Public Library of Science 2023-01-06 /pmc/articles/PMC9821403/ /pubmed/36607999 http://dx.doi.org/10.1371/journal.pone.0280076 Text en © 2023 Yoshimura et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Yoshimura, Takaaki Nishioka, Kentaro Hashimoto, Takayuki Mori, Takashi Kogame, Shoki Seki, Kazuya Sugimori, Hiroyuki Yamashina, Hiroko Nomura, Yusuke Kato, Fumi Kudo, Kohsuke Shimizu, Shinichi Aoyama, Hidefumi Prostatic urinary tract visualization with super-resolution deep learning models |
title | Prostatic urinary tract visualization with super-resolution deep learning models |
title_full | Prostatic urinary tract visualization with super-resolution deep learning models |
title_fullStr | Prostatic urinary tract visualization with super-resolution deep learning models |
title_full_unstemmed | Prostatic urinary tract visualization with super-resolution deep learning models |
title_short | Prostatic urinary tract visualization with super-resolution deep learning models |
title_sort | prostatic urinary tract visualization with super-resolution deep learning models |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9821403/ https://www.ncbi.nlm.nih.gov/pubmed/36607999 http://dx.doi.org/10.1371/journal.pone.0280076 |
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