Abstract #2138

Efficacy of Super-Resolution DLR for Scan Time Reduction with Keeping Image Quality on Female Pelvic MRI as compared with Conventional DLR with ZIP

Takahiro Ueda¹, Natsuka Yazawa², Kaori Yamamoto², Shohei Harada³, Takahiro Matsuyama⁴, Satomu Hanamatsu⁴, Hiroyuki Nagata⁵, Masahiko Nomura¹, Yoshiyuki Ozawa¹, Takeshi Yoshikawa¹, Daisuke Takenaka¹, and Yoshiharu Ohno¹

¹Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Japan, ²Canon Medical Systems Corporation, Otawara, Japan, ³Radiology, Fujita Health University Hospital, Toyoake, Japan, ⁴Radiology, Fujita Health University School of Medicine, Toyoake, Japan, ⁵Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Japan

Synopsis

Keywords: Pelvis, Uterus, Deep learning reconstruction

Motivation: Super-resolution deep learning reconstruction (Precise IQ Engine: PIQE) has a potential to improve spatial resolution and reduce scan time with keeping image quality as compared with conventional deep learning reconstruction (DLR) with ZIP technique.

Goal(s): To determine the efficacy of PIQE for scan time reduction with keeping image quality on female pelvic MRI as compared with conventional DLR with ZIP technique.

Approach: Acquisition time and image quality were statistically compared between low-spatial resolution protocol with PIQE and standard protocol with conventional DLR and ZIP technique.

Results: Mean scan time of PIQE was significantly shorter than that of DLR with ZIP (p<0.001).

Impact: PIQE has a potential to reduce scan time and keep image quality with low-spatial resolution protocol as compared with standard protocol reconstructed by DLR with ZIP on female pelvic MRI.

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