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Abstract #4666

Deep Learning Reconstruction-based Accelerated Rectal MRI: Image Quality, Diagnostic Performance, and Reading Efficiency Assessment

Wenjing Peng1, Lijuan Wan1, Xiaowan Tong1, Fan Yang1, Sicong Wang2, Lin Li1, and Hongmei Zhang1
1Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy, Beijing, China, 2GE Healthcare China, Beijing, China

Synopsis

Keywords: AI/ML Image Reconstruction, Tumor, Rectal adenocarcinoma, Imaging quality, Diagnostic performance

Motivation: Accelerated MRI is an imminent need clinically to satisfy the growing disease burden of rectal cancer.

Goal(s): This study aims to conduct clinical assessment of DLR-based accelerated rectal MRI, encompassing image acquisition, image quality, diagnostic performance, and reding efficiency.

Approach: Two sets of T2WI using standard fast spin-echo (FSEstandard) and DLR-based accelerated FSE (FSEDL) were prospectively compared.

Results: FSEDL showed superior image quality and reading efficiency than FSEstandard, with a 65% reduction in acquisition time. DLR could assist to enhance the accuracy of T-staging for junior radiologists, preserving equivalent diagnostic performance in N staging, EMVI, and MRF.

Impact: This study offered a comprehensive and viable perspective on the application of DLR in rectal MRI, which facilitated improved image quality and reading efficiency, while reducing acquisition time. Moreover, it enhanced the accuracy of T-staging for junior radiologists.

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