Chen, Dongdong

Davies, Mike E.

Golbabaee, Mohammad

Gómez, Pedro A.

Menzel, Marion I.

English

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

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Spatio-temporal regularization for deep MR Fingerprinting

https://www.research.ed.ac.uk/files/146228583/1809.01749v2.pdf

Spatio-temporal regularization for deep MR Fingerprinting

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