Model-based reconstruction of undersampled diffusion tensor k-space data.
Model-based reconstruction of undersampled diffusion tensor k-space data.
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DOI：
10.1002/mrm.24486
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发表时间：
2013-08
影响因子：
3.3
通讯作者：
Hsu, Edward W.
中科院分区：
文献类型：
--
作者：
Welsh, Christopher L.;DiBella, Edward V. R.;Adluru, Ganesh;Hsu, Edward W.
扩散张量成像（DTI）的实际效用，特别是用于离体标本的3D高分辨率自旋扭曲实验，一直受到长采集时间的阻碍。为了加速采集，提出并评估了一种压缩感知框架，该框架采用基于模型的公式来从欠采样的k空间数据重建扩散张量场。将脑标本白色纤维方向、部分各向异性（FA）和平均扩散率（MD）映射的准确性与通过降低图像分辨率、减少扩散编码方向、标准压缩传感或不对称采样重建使用相同扫描时间可实现的替代方法进行比较。所提出的方法的效率也进行了比较，在扩散编码方向的数量的范围内的完全采样的情况下。在一般情况下，所提出的方法被发现，以减少图像模糊和噪声，并提供更准确的纤维取向，FA和MD测量相比，替代方法。此外，根据所使用的欠采样程度和所检查的DTI参数，所提出的方案的测量精度相当于完全采样的DTI数据集，该数据集包括33%至67%的编码方向，并需要成比例地更长的扫描时间。研究结果表明，基于模型的压缩感知有望提高DTI的分辨率，准确性或扫描时间。
The practical utility of diffusion tensor imaging (DTI), especially for 3D high resolution spin warp experiments of ex vivo specimens, has been hampered by long acquisition times. To accelerate the acquisition, a compressed sensing framework that employs a model-based formulation to reconstruct diffusion tensor fields from undersampled k-space data was presented and evaluated. Accuracies in brain specimen white matter fiber orientation, fractional anisotropy (FA) and mean diffusivity (MD) mapping were compared to alternative methods achievable using the same scan time via reduced image resolution, fewer diffusion encoding directions, standard compressed sensing or asymmetrical sampling reconstruction. The efficiency of the proposed approach was also compared to fully-sampled cases across a range of the number of diffusion encoding directions. In general, the proposed approach was found to reduce the image blurring and noise, and provide more accurate fiber orientation, FA and MD measurements compared to the alternative methods. Moreover, depending on the degree of undersampling used and the DTI parameter examined, the measurement accuracy of the proposed scheme was equivalent to fully sampled DTI datasets that consist of 33% to 67% more encoding directions and require proportionally longer scan times. The findings show model-based compressed sensing to be promising for improving the resolution, accuracy or scan time of DTI.
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