Motion-Guided Deep Image Prior for Dynamic Cardiac MRI

Motivation: Real-time free-breathing cardiac MRI requires high acceleration rates and dedicated reconstruction techniques. Deep Learning (DL) methods are often infeasible due to the lack of training data. Goal(s): Develop an unsupervised reconstruction method for dynamic cardiac MRI that can model content variation as well as in-plane and through-plane motion. Approach: Our method is based on the Deep Image Prior (DIP) and combines a low-rank approach with the generation of motion fields. Results: M-DIP outperformed state-of-the-art DIP methods in a phantom and two in-vivo studies. It further achieved similar image quality as a supervised DL method in real-time cine, without requiring a large training dataset. Impact: Our method enables real-time free-breathing cine and free-breathing LGE imaging with high resolution and motion fidelity. It requires no training data and can be extended to other types of dynamic MRI acquisitions.