TY - JOUR
T1 - Sparsity and Locally Low Rank Regularization for Magnetic Resonance Fingerprinting
AU - Lima da Cruz, Gastao Jose
AU - Bustin, Aurelien
AU - Jaubert, Olivier Francois
AU - Schneider, Torben
AU - Botnar, Rene Michael
AU - Prieto Vasquez, Claudia
PY - 2018/12/29
Y1 - 2018/12/29
N2 - Purpose: Develop a sparse and locally low rank regularized reconstruction to accelerate Magnetic Resonance 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, locally low rank regularization has been introduced to exploit temporal redundancy in local regions of the image. Here, we propose to include spatial sparsity and locally low rank 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, T1/T2 phantom acquisition and in vivo brain acquisitions in five healthy subjects with different undersampling factors. Acceleration was also employed 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 artefacts that propagate into the parametric maps. The artefacts 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 one radial spoke per time-point in about 2.6s (~600 time-point images) for 2x2mm and 9.6s (1750 time-point images) for 1x1mm in-plane resolution.
Conclusion: The proposed SLLR-MRF reconstruction further improves parametric map quality compared to low rank MRF, enabling shorter scan times and/or increased spatial resolution.
AB - Purpose: Develop a sparse and locally low rank regularized reconstruction to accelerate Magnetic Resonance 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, locally low rank regularization has been introduced to exploit temporal redundancy in local regions of the image. Here, we propose to include spatial sparsity and locally low rank 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, T1/T2 phantom acquisition and in vivo brain acquisitions in five healthy subjects with different undersampling factors. Acceleration was also employed 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 artefacts that propagate into the parametric maps. The artefacts 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 one radial spoke per time-point in about 2.6s (~600 time-point images) for 2x2mm and 9.6s (1750 time-point images) for 1x1mm in-plane resolution.
Conclusion: The proposed SLLR-MRF reconstruction further improves parametric map quality compared to low rank MRF, enabling shorter scan times and/or increased spatial resolution.
M3 - Article
SN - 0740-3194
JO - Magnetic Resonance in Medicine
JF - Magnetic Resonance in Medicine
ER -