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Abstract #0246

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

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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