Improving model-based functional near-infrared spectroscopy analysis using mesh-based anatomical and light-transport models.
Improving model-based functional near-infrared spectroscopy analysis using mesh-based anatomical and light-transport models.
复制标题
使用基于网格的解剖和光传输模型改进基于模型的功能近红外光谱分析。
DOI：
10.1117/1.nph.7.1.015008
复制
发表时间：
2020
期刊：
影响因子：
5.3
通讯作者：
Fang,Qianqian
中科院分区：
文献类型：
--
作者：
Tran,AnhPhong;Yan,Shijie;Fang,Qianqian
意义：功能近红外光谱（fNIRS）已成为研究人脑的重要工具。通过fNIRS精确量化大脑活动依赖于求解模拟光子在复杂解剖结构中传输的计算模型。目的：我们旨在强调在fNIRS背景下精确解剖建模的重要性，并提出一种用于创建高质量大脑/全头部四面体网格模型的强大方法，用于神经成像分析。方法：我们开发了一种基于表面的大脑网格化管道，与传统的网格化技术相比，它可以产生更好的大脑网格模型。它可以转换成多层表面和四面体网格模型，与典型的处理时间只有几分钟和广泛的实用程序，如在蒙特卡洛或基于有限元的光子模拟fNIRS studies.Results：各种高品质的大脑网格模型已成功地通过处理公开可用的大脑图谱生成。此外，我们比较了三种大脑解剖模型-基于体素的大脑分割，四面体大脑网格和分层板大脑模型-并证明了使用近似大脑解剖结构时大脑部分路径长度的明显差异，范围为-1.5% 结论：在fNIRS研究中，高质量脑网格的生成和使用可以提高脑定量的准确性。我们的开源网格工具箱“Brain 2 Mesh”和“Iso 2 Mesh”可在http://mcx.space/brain2mesh上免费获得。
Significance: Functional near-infrared spectroscopy (fNIRS) has become an important research tool in studying human brains. Accurate quantification of brain activities via fNIRS relies upon solving computational models that simulate the transport of photons through complex anatomy.Aim: We aim to highlight the importance of accurate anatomical modeling in the context of fNIRS and propose a robust method for creating high-quality brain/full-head tetrahedral mesh models for neuroimaging analysis.Approach: We have developed a surface-based brain meshing pipeline that can produce significantly better brain mesh models, compared to conventional meshing techniques. It can convert segmented volumetric brain scans into multilayered surfaces and tetrahedral mesh models, with typical processing times of only a few minutes and broad utilities, such as in Monte Carlo or finite-element-based photon simulations for fNIRS studies.Results: A variety of high-quality brain mesh models have been successfully generated by processing publicly available brain atlases. In addition, we compare three brain anatomical models—the voxel-based brain segmentation, tetrahedral brain mesh, and layered-slab brain model—and demonstrate noticeable discrepancies in brain partial pathlengths when using approximated brain anatomies, ranging between −1.5 % to 23% with the voxelated brain and 36% to 166% with the layered-slab brain.Conclusion: The generation and utility of high-quality brain meshes can lead to more accurate brain quantification in fNIRS studies. Our open-source meshing toolboxes “Brain2Mesh” and “Iso2Mesh” are freely available at http://mcx.space/brain2mesh.