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Sparsity and locally low rank regularization for MR fingerprinting
Author(s) -
Lima da Cruz Gastão,
Bustin Aurélien,
Jaubert Oliver,
Schneider Torben,
Botnar René M.,
Prieto Claudia
Publication year - 2019
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27665
Subject(s) - undersampling , artificial intelligence , computer science , regularization (linguistics) , residual , parametric statistics , computer vision , imaging phantom , pattern recognition (psychology) , redundancy (engineering) , compressed sensing , image resolution , mathematics , algorithm , physics , statistics , optics , operating system
Purpose Develop a sparse and locally low rank (LLR) regularized reconstruction to accelerate MR fingerprinting (MRF). Methods Recent works have introduced low rank reconstructions to MRF, based on temporal compression operators learned from the MRF dictionary. In other MR applications, LLR regularization has been introduced to exploit temporal redundancy in local regions of the image. Here, we propose to include spatial sparsity and LLR regularization terms in the MRF reconstruction. This approach, so called SLLR‐MRF, further reduces aliasing in the time‐point images and enables higher acceleration factors. The proposed approach was evaluated in simulations, T 1 /T 2 phantom acquisition, and in vivo brain acquisitions in 5 healthy subjects with different undersampling factors. Acceleration was also used in vivo to enable acquisitions with higher in‐plane spatial resolution in comparable scan time. Results Simulations, phantom, and in vivo results show that low rank MRF reconstructions with high acceleration factors (<875 time‐point images, 1 radial spoke per time‐point) have residual aliasing artifacts that propagate into the parametric maps. The artifacts are reduced with the proposed SLLR‐MRF resulting in considerable improvements in precision, without changes in accuracy. In vivo results show improved parametric maps for the proposed SLLR‐MRF, potentially enabling MRF acquisitions with 1 radial spoke per time‐point in approximately 2.6 s (~600 time‐point images) for 2 × 2 mm and 9.6 s (1750 time‐point images) for 1 × 1 mm in‐plane resolution. Conclusion The proposed SLLR‐MRF reconstruction further improves parametric map quality compared with low rank MRF, enabling shorter scan times and/or increased spatial resolution.