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Abstract #1048

Robustness of MRI radiomics features in abdomen: impact of deep learning reconstruction and accelerated acquisition

Jingyu Zhong1, Yue Xing1, Yangfan Hu1, Xianwei Liu1, Defang Ding1, Shun Dai1, Junjie Lu2, Yang Song3, Minda Lu3, Huan Zhang4, and Weiwu Yao1
1Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 2Stanford University School of Medicine, Stanford, CA, United States, 3Research Collaboration Team, Siemens Healthineers Ltd., Shanghai, China, 4Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

Synopsis

Keywords: Radiomics, AI/ML Image Reconstruction

Motivation: The deep learning reconstruction and accelerated acquisition using HASTE technique allow scans with comparable image quality and diagnostic performance. However, its impact on MRI-radiomics is unclear.

Goal(s): This study aims to investigate the impact of deep learning reconstruction and accelerated acquisition on the radiomics robustness in abdominal scans using a HASTE sequence.

Approach: Seventeen volunteers were prospectively scanned using deep learning reconstruction and accelerated acquisition protocols, and their influence on radiomics were assessed.

Results: Deep learning reconstruction and accelerated acquisition led to a poor reproducibility of radiomics, but more than a half of them varied within an acceptable range.

Impact: Deep learning reconstruction and accelerated acquisition significantly impacts on radiomic features, necessitating caution to the generalizability when performing radiomic analysis using images from different reconstruction algorithms and acquisition protocols.

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Keywords