Wang Junyan, Shi Yonggang
Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA.
Connect Neuroimaging (2018). 2018 Sep;11083:20-28. doi: 10.1007/978-3-030-00755-3_3. Epub 2018 Sep 15.
Tractography is a prevalent technique for in vivo imaging of the white matter fibers (a.k.a. the tractograms), but it is also known to be error-prone. We previously propose the Group-wse Tractogram Analysis (GiTA) framework for identifying anatomically valid fibers across subjects according to cross-subject consistency. However, the original framework is based on computationally expensive brute-force KNN search. In this work, we propose a more general and efficient extension of GiTA. Our main idea is to find the finite dimensional vector-space representation of the fiber tracts of varied lengths across different subjects, and we call it the group-wise isometric fiber embedding (GIFE). This novel GIFE framework enables the application of the powerful and efficient vector space data analysis methods, such as the k-d tree KNN search, to GiTA. However, the conventional isometric embedding frameworks are not suitable for GIFE due to the massive fiber tracts and the registration errors in the original GiTA framework. To address these issues, we propose a novel method called multidimensional extrapolating (MDE) to achieve GIFE. In our experiment, simulation results show quantitatively that our method outperforms the other methods in terms of computational efficiency/tractability and robustness to errors in distance measurements for real fiber embedding. In addition, real experiment for group-wise optic radiation bundle reconstruction also shows clear improvement in anatomical validity of the results from our MDE method for 47 different subjects from the Human Connectome Project, compared to the results of other fiber embedding methods.
纤维束成像技术是一种用于活体白质纤维（即纤维束）成像的常用技术，但也容易出错。我们之前提出了基于跨个体一致性来识别跨个体解剖学上有效纤维的组-wise纤维束分析（GiTA）框架。然而，原始框架基于计算成本高昂的暴力K近邻搜索。在这项工作中，我们提出了一种更通用、更高效的GiTA扩展方法。我们的主要思想是找到不同个体间不同长度纤维束的有限维向量空间表示，并将其称为组-wise等距纤维嵌入（GIFE）。这种新颖的GIFE框架使得强大且高效的向量空间数据分析方法（如k-d树K近邻搜索）能够应用于GiTA。然而，由于原始GiTA框架中存在大量纤维束和配准误差，传统的等距嵌入框架不适用于GIFE。为了解决这些问题，我们提出了一种名为多维外推（MDE）的新方法来实现GIFE。在我们的实验中，模拟结果定量地表明，在计算效率/可处理性以及对真实纤维嵌入距离测量误差的鲁棒性方面，我们的方法优于其他方法。此外，针对来自人类连接组计划的47个不同个体的组-wise视辐射束重建的实际实验也表明，与其他纤维嵌入方法的结果相比，我们的MDE方法所得结果在解剖学有效性方面有明显改善。
