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Abstract #2444

Deep learning–accelerated HASTE for single breath-hold liver T2-weighted imaging

Kai Liu1, Caixia Fu2, Dominik Nickel3, Caizhong Chen1, Haitao Sun1, and Mengsu Zeng1
1Radiology, Zhongshan Hospital, Fudan University, Shanghai, China, 2MR Collaboration, Siemens (Shenzhen) Magnetic Resonance Ltd., Shenzhen, China, 3MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, Erlangen, Germany

Synopsis

Keywords: Liver, Data Acquisition, Deep-learning reconstructionThe HASTE sequence accelerated by deep learning (DL) reconstruction was used to perform liver T2-weighted imaging under a single breath-hold in a daily routine. Its image quality, including signal-to-noise ratio, contrast-to-noise ratio, artifacts, edge sharpness and slice continuity, was evaluated by comparing it with the conventional multi-breath-hold BLADE sequence. The result demonstrated that DL-accelerated HASTE could shorten the acquisition time remarkably while maintaining clinically satisfactory image quality.

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