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arXiv · 2207.08117

Accelerating Magnetic Resonance T1\r{ho} Mapping Using Simultaneously Spatial Patch-based and Parametric Group-based Low-rank Tensors (SMART)

Yuanyuan Liu·Dong Liang·Zhuo-Xu Cui·Yuxin Yang·Chentao Cao·Qingyong Zhu·Jing Cheng·Caiyun Shi·Haifeng Wang·Yanjie Zhu
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Abstract

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.

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Yuanyuan Liu, Dong Liang, Zhuo-Xu Cui, Yuxin Yang, Chentao Cao, Qingyong Zhu, Jing Cheng, Caiyun Shi, Haifeng Wang, Yanjie Zhu. 2023-01-28. Accelerating Magnetic Resonance T1\r{ho} Mapping Using Simultaneously Spatial Patch-based and Parametric Group-based Low-rank Tensors (SMART). https://arxiv.org/abs/2207.08117

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