Ghosh Aurobrata, Wassermann Demian, Deriche Rachid
Athéna Project Team, INRIA Sophia Antipolis-Méditerranée, France.
Inf Process Med Imaging. 2011;22:723-34. doi: 10.1007/978-3-642-22092-0_59.
A number of non-parametrically represented High Angular Resolution Diffusion Imaging (HARDI) spherical diffusion functions have been proposed to infer more and more accurately the heterogeneous and complex tissue microarchitecture of the cerebral white-matter. These spherical functions overcome the limitation of Diffusion Tensor Imaging (DTI) at discerning crossing, merging and fanning axonal fiber bundle configurations inside a voxel. Tractography graphically reconstructs the axonal connectivity of the cerebral white-matter in vivo and non-invasively, by integrating along the direction indicated by the local geometry of the spherical diffusion functions. Tractography is acutely sensitive to the local geometry and its correct estimation. In this paper we first propose a polynomial approach for analytically bracketing and numerically refining with high precision all the maxima, or fiber directions, of any spherical diffusion function represented non-parametrically. This permits an accurate inference of the fiber layout from the spherical diffusion function. Then we propose an extension of the deterministic Streamline tractography to HARDI diffusion functions that clearly discern fiber crossings. We also extend the Tensorline algorithm to these HARDI functions, to improve on the extended Streamline tractography. We illustrate our proposed methods using the Solid Angle diffusion Orientation Distribution Function (ODF-SA). We present results on multi-tensor synthetic data, and real in vivo data of the cerebral white-matter that show markedly improved tractography results.
为了越来越准确地推断脑白质的异质性和复杂组织微结构，已经提出了许多以非参数形式表示的高角分辨率扩散成像（HARDI）球面扩散函数。这些球面函数克服了扩散张量成像（DTI）在辨别体素内交叉、合并和扇形轴突纤维束构型方面的局限性。纤维束成像通过沿着球面扩散函数的局部几何形状所指示的方向进行积分，以图形方式在体内非侵入性地重建脑白质的轴突连接性。纤维束成像对局部几何形状及其正确估计极为敏感。在本文中，我们首先提出一种多项式方法，用于对以非参数形式表示的任何球面扩散函数的所有最大值或纤维方向进行解析括弧并高精度地进行数值细化。这允许从球面扩散函数准确推断纤维布局。然后，我们将确定性流线型纤维束成像扩展到能够清晰辨别纤维交叉的HARDI扩散函数。我们还将张量线算法扩展到这些HARDI函数，以改进扩展的流线型纤维束成像。我们使用立体角扩散方向分布函数（ODF-SA）来说明我们提出的方法。我们展示了在多张量合成数据以及脑白质的真实体内数据上的结果，这些结果显示纤维束成像结果有显著改善。
