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Abstract #2660

Mapping of exercise-stimulated muscle perfusion using DCE-MRI and an artificial neural network approach

Jeff L Zhang1, Christopher C Conlin2, Xiaowan Li2, Gwenael Layec3,4, Ken Chang1, Jayashree Kalpathy-Cramer1,5, and Vivian S Lee6
1A.A. Martinos Center for Biomedical Imaging; Radiology, Massachusetts General Hospital, Charlestown, MA, United States, 2Radiology and Imaging Science, University of Utah, Salt Lake City, UT, United States, 3Department of Kinesiology, University of Massachusetts, Amherst, MA, United States, 4Institute for Applied Life Sciences, University of Massachusetts, Amherst, MA, United States, 5MGH and BWH Center for Clinical Data Science, Massachusetts General Hospital, Charlestown, MA, United States, 6Verily Life Sciences, Cambridge, MA, United States

We tested the feasibility of using artificial neural network (NN) to rapidly map calf-muscle perfusion, and assessed the importance of data diversity in NN training. Forty-eight DCE MRI data were collected from healthy and diseased subjects stimulated by plantar flexion. Results: the NN method was much faster than model fitting. The NN trained with diverse data gave estimates with mean absolute error (MAE) of 15.9 ml/min/100g, significantly more accurate than regular model fitting or NN trained with homogeneous data (MAE 22.3 and 24.9 ml/min/100g, P<0.001). Conclusion: properly trained NN is capable of estimating muscle perfusion with high accuracy and speed.

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