Abstract #3344

T2-weighted imaging of the prostate with super-resolution deep learning reconstruction: impact on PI-QUAL assessment

Atsushi Nakamoto¹, Hiromitsu Onishi¹, Takahiro Tsuboyama¹, Hideyuki Fukui¹, Takashi Ota¹, Kengo Kiso¹, Toru Honda¹, Shohei Matsumoto¹, Koki Kaketaka¹, Mitsuaki Tatsumi¹, Hiroyuki Tarewaki², Yoshihiro Koyama², Yuichi Yamashita³, Yoshimori Kassai⁴, and Noriyuki Tomiyama¹

¹Osaka University Graduate School of Medicine, Suita, Japan, ²Osaka University Hospital, Suita, Japan, ³Canon Medical Systems, Kawasaki, Japan, ⁴Canon Medical Systems, Otawara, Japan

Synopsis

Keywords: Prostate, Prostate

Motivation: Super-resolution deep learning reconstruction (SR-DLR) can simultaneously reduce noise and improve spatial resolution.

Goal(s): Our goal was to evaluate the usefulness of SR-DLR in prostate T2-weighted imaging (T2WI) with conventional and reduced acquisition times.

Approach: SR-DLR was applied to both conventional acquisition time T2WI and short acquisition time T2WI. Visibility of the anatomical structures of the prostate and image quality were evaluated.

Results: SR-DLR significantly improved image quality of prostate T2WI and visibility of detailed anatomical structures, especially in the small structures such as ejaculatory ducts.

Impact: SR-DLR improves T2WI image quality in prostate MRI and improves the visibility of detailed anatomical structures, and has the potential to reduce acquisition time while maintaining adequate image quality for diagnosis.

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