DSC-derived perfusion map generation from DCE MRI using deep learning

This study built a deep-learning-based method to directly extract DSC MRI perfusion and perfusion related parameters from DCE MRI. A conditional generative adversarial network was modified to solve the pixel-to-pixel perfusion map generation problem. We demonstrate that in both healthy and brain tumor patients, highly realistic perfusion and perfusion related parameter maps can be synthesized from the DCE MRI using this deep-learning method. In healthy controls, the synthesized parameters had distribution similar to the ground truth DSC MRI values. In tumor regions, the synthesized parameters correlated linearly with the ground truth values.