Abstract #0246

Compressed Sensing with and without Deep Learning Reconstruction: Comparison of Capability for Improving Lumber Spine MRI with Parallel Imaging

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

¹Radiology, Fujita Health University School of Medicine, Toyoake, Japan, ²Canon Medical Systems Corporation, Otawara, Japan, ³Radiology, Department of Radiology, Fujita Health University Hospital, Toyoake, Japan, ⁴Radiology, Joint 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) as compared with routinely applied parallel imaging (PI) on lumber spine MRI. We hypothesized that CS with DLR was able to improve image quality and shorten examination time on lumber spine MRI, when compared with PI. The purpose of this study was to directly compare the capability for improving lumber spine MRI among CS with and without DLR and PI in patients with different lumber spinal diseases.

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