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Abstract #2784

Accelerated T2WI for Liver MRI with Deep Learning Reconstruction: A Prospective Comparison on Image Quality and Respiration Factors

Yang Yang1, Chenglin Hu1, Hualing Li1, Qiufeng Liu1, Runyu Tang2, Xiaopeng Song2, and Zhen Li1
1Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, 2Central Research Institute, United Imaging Healthcare, Wuhan, China

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: T2WI liver MRI can lead to long acquisition times and motion artifacts from respiration.

Goal(s): To compare respiratory-gating (RG) and breath-hold (BH) deep learning (DL)-T2WI with traditional T2WI using radial k-space sampling, focusing on the effects of respiratory characteristics on image quality.

Approach: A total of 120 participants were prospectively enrolled to undergo all three T2WI sequences.

Results: DL-T2WI shows comparable image quality to T2WI based on radial k-space sampling strategies. Variation in abdominal wall position at the end of inhalation affect RG T2WI image quality most. For patients with breath-holding difficulties, RG DL-T2WI may be the preferred alternative.

Impact: Respiratory-gating and breath-hold DL-T2WI sequences can be used in daily practice as standard sequences, and parameters derived from the breathing curve can aid in developing a personalized workflow for the imaging process.

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