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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