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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