3D MR fingerprinting with accelerated stack-of-spirals and hybrid sliding-window and GRAPPA reconstruction
Abstract Purpose Whole-brain high-resolution quantitative imaging is extremely encoding intensive, and its rapid and robust acquisition remains a challenge. Here we present a 3D MR fingerprinting (MRF) acquisition with a hybrid sliding-window (SW) and GRAPPA reconstruction strategy to obtain high-resolution T 1 , T 2 and proton density (PD) maps with whole brain coverage in a clinically feasible timeframe. Methods 3D MRF data were acquired using a highly under-sampled stack-of-spirals trajectory with a steady-state precession (FISP) sequence. For data reconstruction, k x -k y under-sampling was mitigated using SW combination along the temporal axis. Non-uniform fast Fourier transform (NUFFT) was then applied to create Cartesian k-space data that are fully-sampled in the in-plane direction, and Cartesian GRAPPA was performed to resolve k z under-sampling to create an alias-free SW dataset. T 1 , T 2 and PD maps were then obtained using dictionary matching. Results Phantom study demonstrated that the proposed 3D-MRF acquisition/reconstruction method is able to produce quantitative maps that are consistent with conventional quantification techniques. Retrospectively under-sampled in vivo acquisition revealed that SW + GRAPPA substantially improves quantification accuracy over the current state-of-the-art accelerated 3D MRF. Prospectively under-sampled in vivo study showed that whole brain T 1 , T 2 and PD maps with 1 mm 3 resolution could be obtained in 7.5 min. Conclusions 3D MRF stack-of-spirals acquisition with hybrid SW + GRAPPA reconstruction may provide a feasible approach for rapid, high-resolution quantitative whole-brain imaging. Highlights Combination of sliding-window and GRAPPA allows highly accelerated 3D MRF. High-resolution (1 mm 3 ) whole-brain multi-parameter maps obtained in 7.5-min. Compared to 2D, 3D MRF enables higher SNR for accurate, isotropic resolution maps. Graphical abstract [DISPLAY OMISSION]
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- DOI : http://dx.doi.org/10.1016/j.neuroimage.2017.08.030
- Elsevier : 저널 > 논문
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