Perfusion is an important aspect of calf muscle function that can be measured with dynamic contrast-enhanced (DCE) MRI. However, conventional methods for quantifying perfusion from DCE-MRI data require an appropriate tracer-kinetic model, which may not be available clinically. In this study, we examined the feasibility of neural networks (NNs) for quantifying calf-muscle perfusion from DCE-MRI data. We found that NNs estimate perfusion with accuracy comparable to conventional methods, without the need for a tracer-kinetic model. NNs like those developed in this study can be readily incorporated into ordinary MRI scanner software, facilitating routine quantitative perfusion analysis with DCE-MRI.