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Prostatic urinary tract visualization with super-resolution deep learning models

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Title: Prostatic urinary tract visualization with super-resolution deep learning models
Authors: Yoshimura, Takaaki Browse this author
Nishioka, Kentaro Browse this author →KAKEN DB
Hashimoto, Takayuki Browse this author →KAKEN DB
Mori, Takashi Browse this author
Kogame, Shoki Browse this author
Seki, Kazuya Browse this author
Sugimori, Hiroyuki Browse this author
Yamashina, Hiroko Browse this author
Nomura, Yusuke Browse this author
Kato, Fumi Browse this author
Kudo, Kohsuke Browse this author
Shimizu, Shinichi Browse this author
Aoyama, Hidefumi Browse this author →KAKEN DB
Issue Date: 6-Jan-2023
Publisher: PLOS
Journal Title: PLoS ONE
Volume: 18
Issue: 1
Start Page: e0280076
Publisher DOI: 10.1371/journal.pone.0280076
Abstract: 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.
Type: article
Appears in Collections:医学院・医学研究院 (Graduate School of Medicine / Faculty of Medicine) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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