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Abstract #3652

Evaluation of deep learning HASTE sequence for liver MRI at 3.0 Tesla: a qualitative and quantitative prospective study

Sanyuan Dong1,2, Shengxiang Rao3, Caizhong Chen3, Mengsu Zeng3, Caixia Fu4, and Dominik Nickel5
1Zhongshan hospital, Fudan university, Shanghai, China, 2Shanghai Institute of Medical Imaging, Shanghai, China, 3Zhongshan Hospital, Fudan University, Shanghai, China, 4MR Collaboration, Siemens (Shenzhen) Magnetic Resonance Ltd., Shenzhen, China, 5MR Application Predevelopment, Siemens Healthineers AG, Erlangen, Germany

Synopsis

Keywords: Liver, Liver

Motivation: Liver T2-weighted imaging usually requires a long scan time. A faster sequence with adequate image qualities is essential in clinical practice.

Goal(s): To evaluate deep-learning reconstruction accelerated T2-weighted HASTE sequence in liver application on the image quality and diagnostic confidence.

Approach: One hundred and five patients were imaged using both sequences. Images were reviewed independently by two blinded observers.

Results: The DL HASTE sequence can detect more liver lesions and improve the CNR of the lesion compared to the conventional T2-weighted BLADE sequence, with a 2.5-fold reduction in acquisition time.

Impact: DL HASTE sequence has the potential to replace the conventional BLADE sequence in routine clinical liver MRI, reducing the scan time and detecting more liver lesions.

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