Deep Learning Reconstruction: Clinical Utility for 3D MRCPs Obtained by Different k-Space Data Acquisition Methods in IPMN Patients
Takahiro Matsuyama1, Yoshiharu Ohno1,2, Kaori Yamamoto3, Masato Ikedo3, Masao Yui3, Saki Takeda4, Akiyoshi Iwase4, Yuka Oshima1, Nayu Hamabuchi1, Satomu Hanamatsu1, Yuki Obama1, Hiroyuki Nagata1, Takahiro Ueda1, Hirotaka Ikeda1, Kazuhiro Murayama2, and Hiroshi Toyama1
1Radiology, Fujita Health University School of Medicine, Toyoake, Japan, 2Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Japan, 3Canon Medical Systems Corporation, Otawara, Japan, 4Radiology, Fujita Health University Hospital, Toyoake, Japan
We hypothesized that deep learning reconstruction (DLR) and multiple k-space data by means of each of the repetition time (TR) techniques (Fast 3D mode multiple: Fast 3Dm) are more useful than parallel imaging (PI) and compressed sensing (CS) for shortening acquisition time and improving image quality and IPMN evaluation capability on 3D MRCP. The purpose of this study was thus to compare the utility of DLR used for PI, Fast 3Dm and CS for improvement of acquisition time, image quality and IPMN evaluation capability on 3D MRCP for patients with IPMN.
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