Quantitative magnetic resonance (MR) parametric mapping is a promising
approach for characterizing intrinsic tissue-dependent information. However,
long scan time significantly hinders its widespread applications. Recently,
low-rank tensor has been employed and demonstrated good performance in
accelerating MR parametricmapping. In this study, we propose a novel method
that uses spatial patch-based and parametric group-based low rank tensors
simultaneously (SMART) to reconstruct images from highly undersampled k-space
data. The spatial patch-based low-rank tensor exploits the high local and
nonlocal redundancies and similarities between the contrast images in
parametric mapping. The parametric group based low-rank tensor, which
integrates similar exponential behavior of the image signals, is jointly used
to enforce the multidimensional low-rankness in the reconstruction process. In
vivo brain datasets were used to demonstrate the validity of the proposed
method. Experimental results have demonstrated that the proposed method
achieves 11.7-fold and 13.21-fold accelerations in two-dimensional and
three-dimensional acquisitions, respectively, with more accurate reconstructed
images and maps than several state-of-the-art methods. Prospective
reconstruction results further demonstrate the capability of the SMART method
in accelerating MR quantitative imaging.Comment: 15 pages, 12 figure