Fully Unsupervised Dynamic MRI Reconstruction via Diffeo-Temporal Equivariance
This addresses the challenge of real-time imaging for cardiac motion and other applications where supervised methods fail due to lack of ground truth, offering an incremental improvement over existing unsupervised approaches.
The paper tackles the problem of reconstructing dynamic MRI sequences from undersampled measurements without ground truth data, proposing an unsupervised framework that leverages geometric spatiotemporal equivariances, and it significantly outperforms state-of-the-art unsupervised methods like SSDU on highly accelerated cardiac imaging.
Reconstructing dynamic MRI image sequences from undersampled accelerated measurements is crucial for faster and higher spatiotemporal resolution real-time imaging of cardiac motion, free breathing motion and many other applications. Classical paradigms, such as gated cine MRI, assume periodicity, disallowing imaging of true motion. Supervised deep learning methods are fundamentally flawed as, in dynamic imaging, ground truth fully-sampled videos are impossible to truly obtain. We propose an unsupervised framework to learn to reconstruct dynamic MRI sequences from undersampled measurements alone by leveraging natural geometric spatiotemporal equivariances of MRI. Dynamic Diffeomorphic Equivariant Imaging (DDEI) significantly outperforms state-of-the-art unsupervised methods such as SSDU on highly accelerated dynamic cardiac imaging. Our method is agnostic to the underlying neural network architecture and can be used to adapt the latest models and post-processing approaches. Our code and video demos are at https://github.com/Andrewwango/ddei.