KEYWORDS: Brain, Monte Carlo methods, Tissues, Photon transport, Scattering, Absorption, Neurons, Energy efficiency, Magnetic resonance imaging, Computer simulations
Transcranial laser stimulation (TLS) is a neural type of photobiomodulation that has been shown beneficial effects on neurons. However, previous research in this field has used multiple wavelengths in the red to near-infrared range. It remains unclear which wavelength is optimal to stimulate the brain. In this study, Monte Carlo simulations are conducted to exposit the efficiencies of three representative wavelengths (660 nm, 810 nm and 1064 nm) in delivering photon energy into the brain. The results indicate that 1064 nm is the optimal, benefiting from its reduced tissue scattering.
Significance: Monte Carlo (MC) light transport simulations are most often performed in regularly spaced three-dimensional voxels, a type of data representation that naturally struggles to represent boundary surfaces with curvature and oblique angles. Not accounting properly for such boundaries with an index of refractivity, mismatches can lead to important inaccuracies, not only in the calculated angles of reflection and transmission but also in the amount of light that transmits through or reflects from these mismatched boundary surfaces.
Aim: A new MC light transport algorithm is introduced to deal with curvature and oblique angles of incidence when simulated photons encounter mismatched boundary surfaces.
Approach: The core of the proposed algorithm applies the efficient preprocessing step of calculating a gradient map of the mismatched boundaries, a smoothing step on this calculated 3D vector field to remove surface roughness due to discretization and an interpolation scheme to improve the handling of curvature.
Results: Through simulations of light hitting the side of a sphere and going through a lens, the agreement of this approach with analytical solutions is shown to be strong.
Conclusions: The MC method introduced here has the advantage of requiring only slight implementation changes from the current state-of-the-art to accurately simulate mismatched boundaries and readily exploit the acceleration of general-purpose graphics processing units. A code implementation, mcxyzn, is made available and maintained at https://omlc.org/software/mc/mcxyzn/.
KEYWORDS: Brain, Tissues, Natural surfaces, 3D modeling, Monte Carlo methods, Neuroimaging, Near infrared spectroscopy, Image segmentation, Skull, Photons
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.
The mobile health field has given rise to a surge of point-of-care diagnostic attachments for mobile phones. These attachments, however, are limited in adoption in low-resource settings due to initial acquisition and subsequent maintenance cost challenges. Point-of-care devices that require no or minimum attachment can make a great impact to the accessibility of such devices in resource-poor regions. In this abstract, we report a simulation study to demonstrate the feasibility of using an ultra-low-cost color-paper filter and a mobile phone to perform broadband pulse oximetry. We run a series of GPU-based Monte Carlo simulations using a previously segmented 7T MRI scan of a finger 3D model. We sweep the optical properties of the finger tissues between the wavelengh band of 400-800 nm with a 1 nm increment, with intensity based on the measured spectrum of an iPhone 8’s LED. We also measured the transmission spectra from paper filters of various colors, which we used to further alter the light source spectrum. Using a discretized photoplethysmogram (PPG) signal, we simulate a 60 bpm oscillation optical measurements due to an up to 15% volume changes of the finger arterioles. Simulations were repeated for various peripheral blood oxygen levels (SpO2). Finally, we estimate the SpO2 using the simulated PPG signals using the Ratio of Ratios (RR) method. We evaluate the performance of different color paper filters by comparing 1) total optical signal intensity, 2) maximum magnitude of the RR signal variations and 3) the correlation of the computed and assumed SpO2 values. We found that the purple-colored filter produced the highest RR signal variations and the cyan-colored paper resulted in the largest SpO2 changes in the tested range.
KEYWORDS: Monte Carlo methods, Scattering, Tissues, Photon transport, Computer simulations, Chemical elements, Data storage, Spherical lenses, Optical components, Geometrical optics
The mesh-based Monte Carlo (MMC) method is an efficient algorithm to model light propagation inside tissues with complex boundaries, but choosing appropriate mesh density can be challenging. A fine mesh improves the spatial resolution of the output but requires more computation. We propose an improved MMC—dual-grid mesh-based Monte Carlo (DMMC)—to accelerate photon simulations using a coarsely tessellated tetrahedral mesh for ray-tracing computation and an independent voxelated grid for output data storage. The decoupling between ray-tracing and data storage grids allows us to simultaneously achieve faster simulations and improved output spatial accuracy. Furthermore, we developed an optimized ray-tracing technique to eliminate unnecessary ray–tetrahedron intersection tests in optically thick mesh elements. We validate the proposed algorithms using a complex heterogeneous domain and compare the solutions with those from MMC and voxel-based Monte Carlo. We found that DMMC with an unrefined constrained Delaunay tessellation of the boundary nodes yielded the highest speedup, ranging from 1.3 × to 2.9 × for various scattering settings, with nearly no loss in accuracy. In addition, the optimized ray-tracing technique offers excellent acceleration in high-scattering media, reducing the ray–tetrahedron test count by over 100-fold. Our DMMC software can be downloaded at http://mcx.space/mmc.
The transcranial photobiomodulation (t-PBM) technique is a promising approach for the treatment of a wide range of neuropsychiatric disorders, including disorders characterized by poor regulation of emotion such as major depressive disorder (MDD). We examine various approaches to deliver red and near-infrared light to the dorsolateral prefrontal cortex (dlPFC) and ventromedial prefrontal cortex (vmPFC) in the human brain, both of which have shown strong relevance to the treatment of MDD. We apply our hardware-accelerated Monte Carlo simulations to systematically investigate the light penetration profiles using a standard adult brain atlas. To better deliver light to these regions-of-interest, we study, in particular, intranasal and transcranial illumination approaches. We find that transcranial illumination at the F3–F4 location (based on 10–20 system) provides excellent light delivery to the dlPFC, while a light source located in close proximity to the cribriform plate is well-suited for reaching the vmPFC, despite the fact that accessing the latter location may require a minimally invasive approach. Alternative noninvasive illumination strategies for reaching vmPFC are also studied and both transcranial illumination at the Fp1–FpZ–Fp2 location and intranasal illumination in the mid-nose region are shown to be valid. Different illumination wavelengths, ranging from 670 to 1064 nm, are studied and the amounts of light energy deposited to a wide range of brain regions are quantitatively compared. We find that 810 nm provided the overall highest energy delivery to the targeted regions. Although our simulations carried out on locations and wavelengths are not designed to be exhaustive, the proposed illumination strategies inform the design of t-PBM systems likely to improve brain emotion regulation, both in clinical research and practice.
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