Abstract #3667

Compressed Sensing and Deep Learning Reconstruction: Efficacy for Women’s Pelvic MRI at 1.5 T MR System in Routine Clinical Practice

Takahiro Ueda¹, Yoshiharu Ohno¹, Kaori Yamamoto², Natsuka Yazawa², Ikki Tozawa³, Masayuki Sato³, Motohiro Katagiri³, Masato Ikedo², Masao Yui², Hiroyuki Nagata¹, Kazuhiro Murayama⁴, and Hiroshi Toyama¹

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

Synopsis

We hypothesized that compressed sensing (CS) with deep learning reconstruction (DLR) can improve image quality and shorten examination time on not only T2-weighted imaging (T2WI), but also T1-weighted imaging (T1WI) on women’s pelvic MRI, when compared with PI at 1.5T MR system. The purpose of this study was to compare the utility of CS and DLR for shortening examination time and improving image quality on MRI at 1.5T system in patients with various female pelvic diseases.

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