Unsupervised Image Reconstruction using Deep Generative Adversarial Networks

Many deep learning-based reconstruction methods require fully-sampled ground truth data for supervised training. However, instances exist where acquiring fully sampled data is either difficult or impossible, such as in dynamic contrast enhancement (DCE), 3D cardiac cine, 4D flow, etc. for training a reconstruction network. We present a deep learning framework for reconstructing MRI without using any fully sampled data. We test the method in two scenarios, and find the method produces higher quality images which reveal vessels and recover more anatomical structure. This method has potential in applications, such as DCE, cardiac cine, low contrast agent imaging, and real-time imaging.