Abstract #0805

Biparametric prostate MRI with super-resolution deep learning reconstruction: image quality assessment using PI-QUAL version 2

Atsushi Nakamoto¹, Toru Honda¹, Shohei Matsumoto¹, Takashi Ota¹, Hideyuki Fukui¹, Kengo Kiso¹, Koki Kaketaka¹, Takumi Tanigaki¹, Hiroyuki Tarewaki², Yoshihiro Koyama², Yuichi Yamashita³, Yoshimori Kassai⁴, Mitsuaki Tatsumi¹, Masatoshi Hori¹, 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, deep learning reconstruction

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

Goal(s): Our goal was to evaluate the image quality of biparametric prostate MRI with SR-DLR using PI-QUAL version 2.

Approach: SR-DLR was applied to both T2WI and DWI. SNR and subjective image quality were compared between SR-DLR and conventional images.

Results: SR-DLR significantly improved the image quality of biparametic prostate MRI and increased PI-QUAL scores.

Impact: SR-DLR improves both T2WI and DWI image quality in prostate MRI, resulting in improved PI-QUAL scores. This technique may have the potential to improve the diagnostic accuracy of prostate biparametric MRI of the prostate.

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