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Model-based reconstruction for T1 mapping using single-shot inversion-recovery radial FLASH
34
Citations
38
References
2016
Year
Image ReconstructionEngineeringBiomedical EngineeringMagnetic Resonance ImagingComputational ImagingDance ImagesTranslational ImagingTime-of-flight ImagingRadiologyHealth SciencesImaging AnatomyReconstruction TechniqueMedical ImagingModel-based ReconstructionImagingNeuroimagingInverse ProblemsSerial Image ReconstructionMedical Image ComputingBiomedical ImagingFunctional X-ray ImagingNeuroscienceQuantitative Parameter MappingExplicit Image Reconstruction
Quantitative parameter mapping in MRI is typically performed as a two-step procedure where serial imaging is followed by pixelwise model fitting. In contrast, model-based reconstructions directly reconstruct parameter maps from raw data without explicit image reconstruction. Here, we propose a method that determines T1 maps directly from multi-channel raw data as obtained by a single-shot inversion-recovery radial FLASH acquisition with a Golden Angle view order. Joint reconstruction of a T1, spin-density and flip-angle map is formulated as a nonlinear inverse problem and solved by the iteratively regularized Gauss-Newton method. Coil sensitivity profiles are determined from the same data in a preparatory step of the reconstruction. Validations included numerical simulations, in vitro MRI studies of an experimental T1 phantom, and in vivo studies of brain and abdomen of healthy subjects at a field strength of 3 T. The results obtained for a numerical and experimental phantom demonstrated excellent accuracy and precision of model-based T1 mapping. In vivo studies allowed for high-resolution T1 mapping of human brain (0.5–0.75 mm in-plane, 4 mm section thickness) and liver (1.0 mm, 5 mm section) within 3.6–5 s. In conclusion, the proposed method for model-based T1 mapping may become an alternative to two-step techniques, which rely on model fitting after serial image reconstruction. More extensive clinical trials now require accelerated computation and online implementation of the algorithm. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 254–263, 2016
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