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

Mohammad Golbabaee, Carolin M. Pirkl, Marion I. Menzel, Guido Buonincontri, Pedro A. Gómez

Research output: Contribution to conferencePaper

Abstract

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.
Original languageEnglish
Publication statusPublished - 16 May 2019
EventISMRM 27th Annual Meeting and Exhibition - Montreal, Canada
Duration: 11 May 201916 May 2019
https://www.ismrm.org/19m/

Conference

ConferenceISMRM 27th Annual Meeting and Exhibition
Abbreviated titleISMRM 2019
CountryCanada
CityMontreal
Period11/05/1916/05/19
Internet address

Fingerprint Dive into the research topics of 'Deep MR Fingerprinting with total-variation and low-rank subspace priors'. Together they form a unique fingerprint.

Cite this