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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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