Ye Wenxing, Entezari Alireza, Vemuri Baba C
CISE Department, University of Florida, Gainesville, FL 32611-6120, USA.
Proc IEEE Int Symp Biomed Imaging. 2010 Apr 14;2010:788-791. doi: 10.1109/ISBI.2010.5490058.
This paper exploits the power of optimal sampling lattices in tomography based reconstruction of the diffusion propagator in diffusion weighted magnetic resonance imaging (DWMRI). Optimal sampling leads to increased accuracy of the tomographic reconstruction approach introduced by Pickalov and Basser [1]. Alternatively, the optimal sampling geometry allows for further reducing the number of samples while maintaining the accuracy of reconstruction of the diffusion propagator. The optimality of the proposed sampling geometry comes from the information theoretic advantages of sphere packing lattices in sampling multidimensional signals. These advantages are in addition to those accrued from the use of the tomographic principle used here for reconstruction. We present comparative results of reconstructions of the diffusion propagator using the Cartesian and the optimal sampling geometry for synthetic and real data sets.
本文利用最优采样格点的优势，在基于断层扫描的扩散加权磁共振成像（DWMRI）中重建扩散传播子。最优采样提高了Pickalov和Basser [1]提出的断层扫描重建方法的准确性。或者，最优采样几何结构允许在保持扩散传播子重建精度的同时进一步减少采样数量。所提出的采样几何结构的最优性源于球体填充格点在多维信号采样中的信息理论优势。这些优势是在此处用于重建的断层扫描原理之外获得的。我们给出了使用笛卡尔采样几何结构和最优采样几何结构对合成数据集和真实数据集进行扩散传播子重建的对比结果。
