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.

中文翻译：

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使用基于网格的解剖模型和光传输模型改进基于模型的功能近红外光谱分析。

意义：功能近红外光谱（fNIRS）已成为研究人脑的重要研究工具。通过fNIRS对大脑活动进行准确的量化依赖于求解计算模型，该模型模拟光子通过复杂解剖结构的传输。目的：我们旨在强调在fNIRS上下文中进行精确解剖建模的重要性，并提出一种可靠的方法来创建用于神经影像分析的高质量大脑/全头四面体网格模型。方法：与常规的网格划分技术相比，我们已经开发了一种基于表面的大脑网格划分管道，该管道可以产生更好的大脑网格划分模型。它可以将分段的体积脑部扫描转换为多层表面和四面体网格模型，典型处理时间仅为几分钟，并且用途广泛，例如在Monte Carlo中或基于fNIRS研究的基于有限元的光子模拟中。结果：通过处理公开可用的脑图集已成功生成了各种高质量的脑网格模型。此外，我们比较了三种大脑解剖模型-基于体素的脑分割，四面体脑网格和分层平板脑模型-并证明了在使用近似的大脑解剖结构时，大脑局部光程的明显差异，介于-1.5％至23％之间使用体素化大脑，分层板状大脑占36％至166％。结论：在fNIRS研究中，高质量脑网格的生成和实用性可以导致更准确的脑量化。我们的开源网格划分工具箱“ Brain2Mesh”和“ Iso2Mesh”可从http://mcx.space/brain2mesh免费获得。