Abstract

.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 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.

Highlights

  • Functional near-infrared spectroscopy has played an increasingly important role in functional neuroimaging.[1]

  • We use a sample fullhead mesh generated from the Magnetic Resonance Imaging (MRI) database and report their differences in key optical parameters by performing 3-D mesh, voxel- and layered-domain Monte Carlo (MC) transport simulations at a range of source–detector (SD) separations

  • 3.1 High-Quality Tetrahedral Meshes of Human Head and Brain Models In Fig. 4, a sample full-head mesh model is generated from an Statistical Parametric Mapping (SPM) segmentation with tissue priors from the Laboratory for Research in Neuroimaging.[52]

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Summary

Introduction

Functional near-infrared spectroscopy (fNIRS) has played an increasingly important role in functional neuroimaging.[1] Using light in the red and near-infrared range, the hemodynamic response of the brain is probed through careful placement of sources and detectors on the scalp surface at multiple wavelengths. Intensities at the detectors that are used to infer the locations of these activities The accuracy of this inference depends greatly upon an accurate representation of the complex human brain anatomy and on the surrounding tissues that affect the migrations of photons from the sources to the detectors. While much simplified brain models, such as planar[2,3] or spherical layers,[4] as well as approximated photon propagation models, such as the diffusion approximation (DA),[5] have been widely utilized by the research community, their limitations are recognized by a number of studies.[3,6] In addition, modeling brain anatomy accurately plays important roles in other quantitative neuroimaging modalities, such as electroencephalography (EEG)[7] and magnetoencephalography.[8]

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