In the treatment planning for urethra-sparing radiation therapy in localized prostate cancer, it is important to visualize the prostatic urinary tract to reduce the risk of urinary symptoms linked to the urethral dose. We developed a methodology for visualizing the prostatic urinary tract by post-urination magnetic resonance imaging (PU-MRI) without using a urethral catheter for urethra-sparing radiotherapy. Several super-resolution (SR) deep learning models were proposed for improving image quality. This study investigated whether these SR deep learning models improve the visibility of the prostatic urinary tract in PU-MRI. Materials/Methods: We enrolled 30 patients who had previously undergone real-time-image-gated spot scanning proton therapy (RGPT) by inserting fiducial markers at our institution from October 2019 to October 2020. PU-MRI was performed using a non-contrast high-resolution two-dimensional T2-weighted turbo spin-echo (TSE) imaging sequence. Four different SR deep learning models were constructed: the EDSR (enhanced deep SR network), WDSR (widely activated SR network), SRGAN (SR generative adversarial network), and RDN (residual dense network). All models were trained using a diverse 2K resolution high-quality image dataset. We imported PU-MRIs as lowresolution images and exported four SR images that were enlarged using one of the models each. To assess the performance of the proposed SR image compared to PU-MRI, we used the complex wavelet structural similarity index measure (CW-SSIM) as the quantitative metric. A 1-to-5 scale was used to subjectively evaluate the visibility of the prostatic urinary tract by two radiation oncologists. Results: The mean ( § standard deviation) of the CW-SSIM in the EDSR, WDSR, SRGAN, and RDN were 0.999 § 0.001, 0.999 § 0.002, 0.993 § 0.002, and 0.997 § 0.002, respectively. The mean prostatic urinary tract visibility scores for oncologist 1 and 2 were 3.70 § 0.64 and 3.53 § 0.99 for PU-MRI, 3.67 § 0.60 and 2.70 § 0.82 for EDSR, 3.70 § 0.53 and 2.73 § 0.81 for WDSR, 3.67 § 0.65 and 2.73 § 0.81 for SRGAN, and 4.37 § 0.61 and 3.73 § 0.93 for RDN, respectively. Conclusion: This study investigated whether the combination of PU-MRI and SR deep learning models improves visibility of the prostatic urinary tract. The results suggest that SR images using RDN are highly similar to the original image and subjectively improve the visibility of the prostatic urinary tract.

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