Deep MR Fingerprinting with total-variation and low-rank subspace priors
This work addresses reconstruction challenges in medical imaging for MRF applications, representing an incremental improvement over existing deep learning methods.
The paper tackled the problem of undersampling artifacts in deep learning-based Magnetic Resonance Fingerprinting reconstruction by proposing an accelerated iterative method with convex regularization to promote spatio-temporal regularities, resulting in improved reconstruction validated on synthetic and in-vivo datasets.
Deep learning (DL) has recently emerged to address the heavy storage and computation requirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprinting (MRF) reconstruction. Fed with non-iterated back-projected images, the network is unable to fully resolve spatially-correlated corruptions caused from the undersampling artefacts. We propose an accelerated iterative reconstruction to minimize these artefacts before feeding into the network. This is done through a convex regularization that jointly promotes spatio-temporal regularities of the MRF time-series. Except for training, the rest of the parameter estimation pipeline is dictionary-free. We validate the proposed approach on synthetic and in-vivo datasets.