2022/07/17 by Yuanyuan Liu, Dong Liang, Liu, Yuanyuan +17
Medicine · #Advanced MRI Techniques and Applications #Advanced Neuroimaging Techniques and Applications #FOS: Electrical engineering #Fetal and Pediatric Neurological Disorders #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2207.08117
openalex publication_date 2022/07/17 · openalex created_date 2022/07/21 · openalex updated_date 2026/07/28
Quantitative magnetic resonance (MR) T1\rho 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\rho 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\rho 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\rho imaging.