Online dynamic MRI reconstruction via robust subspace tracking
We propose an efficient online reconstruction algorithm for the problem of highly undersampled dynamic magnetic resonance imaging (DMRI). Our approach reconstructs the dynamic time series by processing only a small batch of frames at a time. We adapt an online subspace tracking algorithm based on manifold optimization to the DMRI reconstruction setting and propose a novel extension of the algorithm to enable robust subspace tracking based on a local low-rank plus transform sparse model. Our experiments on real and synthetic data show that proposed approach gives comparable results to methods that reconstruct the entire image series at once while requiring only a fraction of the memory and computational demand. The dramatic memory savings allows robust subspace-based methods to be applied to much larger datasets than previously allowed.