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Model-based T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH.

MPS-Authors
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Wang,  X.
Biomedical NMR Research GmbH, MPI for Biophysical Chemistry, Max Planck Society;

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Roeloffs,  V. B.
Biomedical NMR Research GmbH, MPI for Biophysical Chemistry, Max Planck Society;

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Klosowski,  J.
Biomedical NMR Research GmbH, MPI for Biophysical Chemistry, Max Planck Society;

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Tan,  Z.
Biomedical NMR Research GmbH, MPI for Biophysical Chemistry, Max Planck Society;

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Voit,  D.
Biomedical NMR Research GmbH, MPI for Biophysical Chemistry, Max Planck Society;

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Frahm,  J.
Biomedical NMR Research GmbH, MPI for Biophysical Chemistry, Max Planck Society;

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2457178_Suppl.docx
(Supplementary material), 791KB

Citation

Wang, X., Roeloffs, V. B., Klosowski, J., Tan, Z., Voit, D., Uecker, U., et al. (2018). Model-based T1 mapping with sparsity constraints using single-shot inversion-recovery radial FLASH. Magnetic Resonance in Medicine, 79(2), 730-740. doi:10.1002/mrm.26726.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002D-8027-4
Abstract
PURPOSE: To develop a model-based reconstruction technique for single-shot T1 mapping with high spatial resolution, accuracy, and precision using an inversion-recovery (IR) fast low-angle shot (FLASH) acquisition with radial encoding. METHODS: 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 T1 phantom, as well as in vivo studies of the human brain and liver at 3 T. RESULTS: In comparison to previous reconstruction methods for single-shot T1 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. CONCLUSION: The comprehensive model-based reconstruction with L1 regularization offers rapid and robust T1 mapping with high accuracy and precision. The method warrants accelerated computing and online implementation for extended clinical trials. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.