Spatio-temporal regularization for deep MR Fingerprinting

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

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

We study a deep learning approach to address the heavy storage and computation re- quirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprint- ing (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 ap- pear 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 languageUndefined/Unknown
Number of pages4
Publication statusPublished - 10 Jul 2019
EventThe 2nd International Conference on Medical Imaging with Deep Learning (MIDL) -
Duration: 1 Jul 2019 → …


ConferenceThe 2nd International Conference on Medical Imaging with Deep Learning (MIDL)
Period1/07/19 → …

Keywords / Materials (for Non-textual outputs)

  • magnetic resonance fingerprinting
  • deep learning
  • Regularisation

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