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Abstract #4116

Deep Learning-accelerated, Single-breath-hold T2-weighted Imaging for Tumor Invasion Assessment in Gastric Cancer

Wei-Yue Xu1, Qiong Li1, Ya-Jun Hou1, Yi-Cheng Hsu2, Dominik Nickel3, Yu-Dong Zhang1, and Xi-Sheng Liu1
1Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China, 2MR Collaboration, Siemens Healthineers Ltd, Shanghai, China, 3MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany

Synopsis

Keywords: Contrast Mechanisms, Contrast Mechanisms, deep learning-accelerated T2WIT2-weighted imaging (T2WI) is an indispensable sequence of gastric magnetic resonance imaging (MRI) for tumor assessment. This study compared the deep learning-accelerated T2WI sequence (DL-T2WI) with BLADE T2WI in image quality assessment and tumor invasion evaluation of gastric cancer (GC). It revealed that DL-T2WI, acquired within a single-breath-hold, performed better than BLADE T2WI in terms of image quality for both quantitative and qualitative analyses. Furthermore, DL-T2WI images displayed comparable accuracy to BLADE T2WI images for serous invasion evaluation. The study suggests that DL-T2WI might be superior to BLADE T2WI for GC.

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