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 Layec^3,4, Ken Chang¹, Jayashree Kalpathy-Cramer^1,5, 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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