  Quantitative magnetic resonance (MR) T1\r{ho} 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
T1\r{ho} mapping. 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 T1\r{ho} 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 T1\r{ho}
imaging.
