Spatio-temporal regularization for deep MR Fingerprinting

Mohammad Golbabaee, Dongdong Chen, Marion I. Menzel, Mike Davies, Pedro A. Gómez

Research output: Contribution to conferencePaperpeer-review


We study a deep learning approach to address the heavy storage and computation requirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprinting (MRF) reconstruction. The MRF-Net provides a piece-wise affine approximation to the (temporal) Bloch response manifold projection. Fed with non-iterated back-projected images, the network alone is unable to fully resolve spatially-correlated artefacts which appear in highly undersampling regimes. 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.
Original languageEnglish
Publication statusPublished - 10 Jul 2019
EventMedical Imaging with Deep Learning - London, UK United Kingdom
Duration: 8 Jul 201910 Jul 2019


ConferenceMedical Imaging with Deep Learning
Abbreviated titleMIDL 2019
Country/TerritoryUK United Kingdom
Internet address


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