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.