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.
|Publication status||Published - 16 May 2019|
|Event||ISMRM 27th Annual Meeting and Exhibition - Montreal, Canada|
Duration: 11 May 2019 → 16 May 2019
|Conference||ISMRM 27th Annual Meeting and Exhibition|
|Abbreviated title||ISMRM 2019|
|Period||11/05/19 → 16/05/19|
Golbabaee, M., Pirkl, C. M., Menzel, M. I., Buonincontri, G., & Gómez, P. A. (2019). Deep MR Fingerprinting with total-variation and low-rank subspace priors. Paper presented at ISMRM 27th Annual Meeting and Exhibition, Montreal, Canada. https://arxiv.org/abs/1902.10205