Abstract #2523

Compressed Sensing with and without Deep Learning Reconstruction: Comparison of the Utility for Women’s Pelvic MRI with Parallel Imaging

Takahiro Ueda¹, Yoshiharu Ohno¹, Kaori Yamamoto², Akiyoshi Iwase³, Takashi Fukuba³, Yuki Obama¹, Kazuhiro Murayama⁴, and Hiroshi Toyama¹

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

There have been no major reports for assessing the utility of compressed sensing (CS) and deep learning reconstruction (DLR) on women’s pelvic MRI as compared with routinely applied parallel imaging (PI). We hypothesized that CS with DLR was able to improve image quality and shorten examination time on women’s pelvic MRI, when compared with PI. The purpose of this study was to directly compare the utility of CS and DLR with PI at women’s pelvic MRI examination in patients with different women’s pelvic diseases.

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