For accurate fitting of muscle diffusion tensor imaging data, many methods have been proposed. In this study, the performance of an unsupervised physics-informed deep learning method for IVIM-DTI fitting of muscle DTI data is investigated. The neural net comprised 9 fully connected networks and was tested on 20 upper leg DWI datasets. It trained in 45s and fitting a full dataset took around 4s. Although the parameter maps of the traditional and NN fitting look similar all parameters were significantly different. The network is capable of fitting the model within seconds but the differences need to be further investigated.