Abstract #0609

Deep Learning Reconstruction: Clinical Utility for 3D MRCPs Obtained by Different k-Space Data Acquisition Methods in IPMN Patients

Takahiro Matsuyama¹, Yoshiharu Ohno^1,2, 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

Synopsis

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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