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Accelerating Magnetic Resonance T 1ρ Mapping Using Simultaneously Spatial Patch-Based and Parametric Group-Based Low-Rank Tensors (SMART).
- Source :
-
IEEE transactions on medical imaging [IEEE Trans Med Imaging] 2023 Aug; Vol. 42 (8), pp. 2247-2261. Date of Electronic Publication: 2023 Aug 01. - Publication Year :
- 2023
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Abstract
- Quantitative magnetic resonance (MR) [Formula: see text] mapping is a promising approach for characterizing intrinsic tissue-dependent information. However, long scan time significantly hinders its widespread applications. Recently, low-rank tensor models have been employed and demonstrated exemplary performance in accelerating MR [Formula: see text] mapping. This study proposes 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 [Formula: see text] mapping. The parametric group-based low-rank tensor, which integrates similar exponential behavior of the image signals, is jointly used to enforce multidimensional low-rankness in the reconstruction process. In vivo brain datasets were used to demonstrate the validity of the proposed method. Experimental results 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 [Formula: see text] imaging.
Details
- Language :
- English
- ISSN :
- 1558-254X
- Volume :
- 42
- Issue :
- 8
- Database :
- MEDLINE
- Journal :
- IEEE transactions on medical imaging
- Publication Type :
- Academic Journal
- Accession number :
- 37027549
- Full Text :
- https://doi.org/10.1109/TMI.2023.3246113