Meeting Banner
Abstract #0807

Deep MR Fingerprinting with total-variation and low-rank subspace priors

Mohammad Golbabaee1, Carolin M. Pirkl2,3, Marion I. Menzel3, Guido Buonincontri4, and Pedro A. Gómez3,5

1Computer Science department, University of Bath, Bath, United Kingdom, 2Computer Science department, Technische Universität München, Munich, Germany, 3GE Healthcare, Munich, Germany, 4Imago7 Foundation, Pisa, Italy, 5School of Bioengineering, Technische Universität München, Munich, Germany

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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here