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

Prostatic urinary tract visualization with super-resolution deep learning models

Yoshimura, Takaaki; Nishioka, Kentaro; Hashimoto, Takayuki et al.

PLoS ONE, 2023, 18(1), e0280076

Permalink : https://hdl.handle.net/2115/88864

このアイテムのアクセス数:217件（2026-01-15 12:34 集計）

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https://doi.org/10.1371/journal.pone.0280076

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アクセス権	metadata only access
URI	https://hdl.handle.net/2115/88864
タイトル	Prostatic urinary tract visualization with super-resolution deep learning models
著者	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
著者名 Yoshimura, Takaaki
e-Rad_Researcher 80463743 著者名 Nishioka, Kentaro
e-Rad_Researcher 60400678 著者名 Hashimoto, Takayuki
著者名 Mori, Takashi
著者名 Kogame, Shoki
著者名 Seki, Kazuya
著者名 Sugimori, Hiroyuki
著者名 Yamashina, Hiroko
著者名 Nomura, Yusuke
著者名 Kato, Fumi
著者名 Kudo, Kohsuke
著者名 Shimizu, Shinichi
e-Rad_Researcher 80360915 著者名 Aoyama, Hidefumi
言語	英語
発行日	2023-01-06
出版者	PLOS
収録物名	PLoS ONE
巻(号)	18(1)
ページ	e0280076
抄録	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.
資源タイプ	学術雑誌論文
出版タイプ	Version of Record
PISSN	1932-6203
関連情報 (isIdenticalTo)	出版社版DOI https://doi.org/10.1371/journal.pone.0280076