Model‐based T 1 mapping with sparsity constraints using single‐shot inversion‐recovery radial FLASH

• Published in: Magnetic resonance in medicine Vol. 79; no. 2; pp. 730 - 740
• Main Authors: Wang, Xiaoqing, Roeloffs, Volkert, Klosowski, Jakob, Tan, Zhengguo, Voit, Dirk, Uecker, Martin, Frahm, Jens
• Format: Journal Article
• Language: English
• Published: United States 01.02.2018
• Subjects: Algorithms; Brain - diagnostic imaging; Brain Mapping - methods; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Phantoms, Imaging; T1 mapping; parallel imaging; Look-Locker; sparsity constraint; model-based reconstruction
• ISSN: 0740-3194; 1522-2594
• DOI: 10.1002/mrm.26726

To develop a model-based reconstruction technique for single-shot T mapping with high spatial resolution, accuracy, and precision using an inversion-recovery (IR) fast low-angle shot (FLASH) acquisition with radial encoding. The proposed model-based reconstruction jointly estimates all model parameters, that is, the equilibrium magnetization, steady-state magnetization, 1/ T1*, and all coil sensitivities from the data of a single-shot IR FLASH acquisition with a small golden-angle radial trajectory. Joint sparsity constraints on the parameter maps are exploited to improve the performance of the iteratively regularized Gauss-Newton method chosen for solving the nonlinear inverse problem. Validations include both a numerical and experimental T phantom, as well as in vivo studies of the human brain and liver at 3 T. In comparison to previous reconstruction methods for single-shot T mapping, which are based on real-time MRI with pixel-wise fitting and a model-based approach with a predetermination of coil sensitivities, the proposed method presents with improved robustness against phase errors and numerical precision in both phantom and in vivo studies. The comprehensive model-based reconstruction with L1 regularization offers rapid and robust T mapping with high accuracy and precision. The method warrants accelerated computing and online implementation for extended clinical trials. Magn Reson Med 79:730-740, 2018. © 2017 International Society for Magnetic Resonance in Medicine.