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Abstract #0820

Deep learning based MR fingerprinting ASL ReconStruction (DeepMARS)

Qiang Zhang1, Pan Su2, Ying Liao3, Rui Guo1, Haikun Qi4, Zhangxuan Hu1, Hanzhang Lu2, and Huijun Chen1

1Tsinghua University, Beijing, China, 2Johns Hopkins University, Baltimore, MD, United States, 3New York University, New York, NY, United States, 4School of Biomedical Engineering and Imaging Sciences, King's College London, London, London, United Kingdom

The purpose of this study is to develop a MRF-ASL reconstruction algorithm using deep learning (DeepMARS). Compared with the traditional dictionary matching, our DeepMARS achieved higher intra-class correlation (ICC) in B1 (0.971 vs 0.921) and BAT (0.926 vs 0.761), similar ICC in T1 (0.957 vs 0.964) and CBF (0.936 vs 0.948) in the reproducibility test with much shorter calculation time per voxel (0.368 ms vs 2.899 s), suggesting that our DeepMARS may be a better alternative than the conventional MR dictionary matching approach.

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