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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