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.
|Publication status||Published - 10 Jul 2019|
|Event||Medical Imaging with Deep Learning - London, UK United Kingdom|
Duration: 8 Jul 2019 → 10 Jul 2019
|Conference||Medical Imaging with Deep Learning|
|Abbreviated title||MIDL 2019|
|Country||UK United Kingdom|
|Period||8/07/19 → 10/07/19|