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Adaptive diffusion basis functions decomposition for estimating intra-voxel myocardium fiber geometry

机译:自适应扩散基函数分解以估计体素心肌纤维的几何形状

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Diffusion basis functions decomposition for recovering intra-voxel fiber tract geometry from diffusion weighted MRI data has been proposed. In this formulation, the intra-voxel information is recovered at voxels containing fiber crossings or branching via the use of a linear combination of a discrete set of diffusion basis functions. Then, the parametric representation of the intra-voxel fiber geometry is described as a discrete mixture of Gaussians. The eigenvalues of tensor basis are estimated at the same time as the rest of the unknown parameters by employing an adaptive approach. The performance of our method is evaluated using both simulated and real diffusion-weighed MR data and compared with existing approach. The results show that our adaptive method outperforms existing method.
机译:提出了基于扩散加权MRI数据恢复体素纤维束几何形状的扩散基函数分解。在此公式中,通过使用一组离散的扩散基函数的线性组合,在包含纤维交叉或分支的体素中恢复体素内部信息。然后,将体素内部纤维几何形状的参数表示形式描述为高斯离散分布。通过采用自适应方法,与其他未知参数同时估计张量基特征值。我们的方法的性能是通过模拟和实际扩散加权MR数据评估的,并与现有方法进行了比较。结果表明,我们的自适应方法优于现有方法。

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