Rapid Diffusion-Weighted Magnetic Resonance Imaging Of The Brain Without Susceptibility Artifacts: Single-Shot Steam With Radial Undersampling And Iterative Reconstruction (Vol 7, Pg 428, 2017)

INVESTIGATIVE RADIOLOGY(2019)

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摘要
Objective: The aim of this study was to develop a rapid diffusion-weighted (DW) magnetic resonance imaging (MRI) technique for whole-brain studies without susceptibility artifacts and measuring times below 3 minutes.Materials and Methods: The proposed method combines a DWspin-echo module with a single-shot stimulated echo acquisition mode MRI sequence. Previous deficiencies in image quality due to limited signal-to-noise ratio are compensated for (1) by radial undersampling to enhance the flip angle and thus the signal strength of stimulated echoes; (2) by defining the image reconstruction as a non-linear inverse problem, which is solved by the iteratively regularized Gauss-Newton method; and (3) by denoising with use of a modified nonlocal means filter. The method was implemented on a 3 TMRI system(64-channel head coil, 80 mT . m gradients) and evaluated for 10 healthy subjects and 2 patients with an ischemic lesion and epidermoid cyst, respectively.Results: High-quality mean DW images of the entire brain were obtained by acquiring 1 non-DW image and 6 DW images with different diffusion directions at b = 1000 s . mm. The achievable resolution for a total measuring time of 84 seconds was 1.5 mm in plane with a section thickness of 4 mm (55 sections). A measuring time of 168 seconds allowed for an in-plane resolution of 1.25 mm and a section thickness of 3 mm (54 sections). Apparent diffusion coefficient values were in agreement with literature data.Conclusions: The proposed method for DWMRI offers immunity against susceptibility problems, high spatial resolution, adequate signal-to-noise ratio and clinically feasible scan times of less than 3 minutes for whole-brain studies. More extended clinical trials require accelerated computation and online reconstruction.
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关键词
diffusion-weighted MRI, radial sampling, iterative reconstruction, Gauss-Newton method
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