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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Medicine / Faculty of Medicine >
Peer-reviewed Journal Articles, etc >
Prostatic urinary tract visualization with super-resolution deep learning models
| 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 |
| URI: | http://hdl.handle.net/2115/88864 |
| 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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