Unsupervised MRI Reconstruction with Generative Adversarial Networks

Understand

Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications.

• However, most approaches require large-scale fully-sampled ground truth data for supervised training.
• Acquiring fully-sampled data is often either difficult or impossible, particularly for dynamic contrast enhancement (DCE), 3D cardiac cine, and 4D flow.
• We present a deep learning framework for MRI reconstruction without any fully-sampled data using generative adversarial networks.