Flow-based Geometric Interpolation of Fiber Orientation Distribution Functions.

Flow-based Geometric Interpolation of Fiber Orientation Distribution Functions.
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纤维取向分布函数的基于流的几何插值。

DOI:
10.1007/978-3-031-43993-3_5
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发表时间:
2023
期刊:
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子:
--
通讯作者:
Shi,Yonggang
Shi,Yonggang
中科院分区:
--
文献类型:
--
作者:
Nie,Xinyu;Shi,Yonggang

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纤维取向分布函数(FOD)是一种代表复杂纤维几何结构的高角分辨率扩散磁共振成像的先进模型。然而,FOD函数复杂的数学结构给FOD图像处理任务带来了挑战,如内插,它在纤维束成像中起着至关重要的作用。在基于FOD的纤维束成像中,为了提高数值效率,通常使用线性内插,但它容易产生虚假的人工信息,导致解剖学上不正确的纤维束。为了克服这一困难,我们提出了一种基于流的几何一致的插值框架,该框架考虑了每个位置邻域内FOD的峰值旋转。我们的方法将FOD函数分解成多个分量,并使用平滑向量场来模拟每个峰值在其邻域内的流动。为了生成沿每个矢量场流动的内插结果,我们提出了一种闭合形式的高效方法来旋转相邻体素中的FOD峰值,并实现FOD分量的几何一致内插。通过结合每个峰值的内插结果,我们得到了最终的FOD内插。在人类连接组计划(HCP)数据上的实验结果表明,我们的方法产生了更具解剖学意义的FOD内插,并显著提高了纤维束成像的性能。
The fiber orientation distribution function (FOD) is an advanced model for high angular resolution diffusion MRI representing complex fiber geometry. However, the complicated mathematical structures of the FOD function pose challenges for FOD image processing tasks such as interpolation, which plays a critical role in the propagation of fiber tracts in tractography. In FOD-based tractography, linear interpolation is commonly used for numerical efficiency, but it is prone to generate false artificial information, leading to anatomically incorrect fiber tracts. To overcome this difficulty, we propose a flow-based and geometrically consistent interpolation framework that considers peak-wise rotations of FODs within the neighborhood of each location. Our method decomposes a FOD function into multiple components and uses a smooth vector field to model the flows of each peak in its neighborhood. To generate the interpolated result along the flow of each vector field, we develop a closed-form and efficient method to rotate FOD peaks in neighboring voxels and realize geometrically consistent interpolation of FOD components. By combining the interpolation results from each peak, we obtain the final interpolation of FODs. Experimental results on Human Connectome Project (HCP) data demonstrate that our method produces anatomically more meaningful FOD interpolations and significantly enhances tractography performance.
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