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Abstract #4650

Validation of a deep-learning-based method for accelerating susceptibility-weighted imaging in clinical subjects

Xiao Wu1, Shan Xu2, Yao Zhang2, Jianzhong Sun2, and Peiyu Huang2
1Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China, 2The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

Synopsis

Keywords: Data Acquisition, Machine Learning/Artificial Intelligence, deep-learning ,susceptibility-weighted imaging,magnetic resonance imaging In this study, we validated a deep-learning-based method for accelerating susceptibility-weighted imaging (SWI) in 31 clinical subjects. Compared to the fully sampled images, the accelerated SWI images had less noise and imaging artifacts. Although the images had decreased sharpness, the anatomical details of the lesions were mostly kept, and we had not observed false negative\positive lesions. This method could be useful for clinical situations that need timely imaging results.

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