Three dimensional Monte Carlo code for photon migration through complex heterogeneous media including the adult human head.

By David A. Boas, Joseph P. Culver, J. Stott, Andrew K. Dunn. Optics Express, 2002

672 citations recorded by the source corpus.

We describe a novel Monte Carlo code for photon migration through 3D media with spatially varying optical properties. The code is validated against analytic solutions of the photon diffusion equation for semi-infinite homogeneous media. The code is also cross-validated for photon migration through a slab with an absorbing heterogeneity. A demonstration of the utility of the code is provided by showing time-resolved photon migration through a human head. This code, known as 'tMCimg', is available on the web and can serve as a resource for solving the forward problem for complex 3D structural data obtained by MRI or CT.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c564d4389e70d11fdba43d4f79aa07335f8188d2

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In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: a computer simulation feasibility study (2005, background): “An open source MC code, originally intended for computing the time-resolved reflectance from photon sources located on the surface of optically heterogeneous media (Boas et al 2002), was modified to simulate the migration of photons emanating from sources at the mouse torso interior.”
Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters (2003, background): “Details about the Monte Carlo code can be found in Boas et al. (2002).”
Statistical analysis of fNIRS data: A comprehensive review (2014, neutral): “…the optical density changes in a scattering medium can be derived from the photon diffusion equation using the Rytov approximation (Arridge, 1999; Boas et al., 2002, 2004; Ye et al., 2009): ΔOD r s ; r d ; λ; t ð Þ ¼ − ln Φ r s ; r d ; λ; t ð Þ Φ 0 r s ; r d ; λ; t ð Þ ≈∫ Φ 0 r s ; r; λ ð Þ Φ 0…”
Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration. (2008, positive): “Several authors have, for example, sed MC to simulate photon migration in complex structures, uch as the adult human head.(1) The technique has also been sed to understand the generation of fluorescence and Raman ignals in random media.”
HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. (2009, methodology): “Although the propagation of light through tissue can either be modeled by using light transport theory [13,14,16] or be measured experimentally [83,90], most NIRS experiments do not have access to this information, as this generally requires an additional MRI or computed axial tomography scan to pro”
Anatomical guidance for functional near-infrared spectroscopy: AtlasViewer tutorial (2015, background): “Table 1 Default optical properties of different tissue types in the AtlasViewer head model.(25)”
Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy. (2004, background): “The forward solution of the diffusion equation can be obtained for arbitrary tissue geometries with spatially varying optical properties using finite-difference (Barnett et al., 2003; Hielscher et al., 1998), finite-element (Arridge et al., 1993; Okada et al., 1996a; Paulsen and Jiang, 1995), and Mo”
Review of Monte Carlo modeling of light transport in tissues (2013, neutral): “Boas et al.37 proposed a voxel-based 3-”
The NIRS Brain AnalyzIR Toolbox (2018, methodology): “Parts of the toolbox interface to fNIRS forward models (models of the light propagation through tissue/brain) require separate download of the software such as NIRFAST (Near-Infrared light transport in tissue and image reconstruction) [21,22], Matlab/Octave-based mesh generation toolbox (Iso2mesh) [”
Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units (2009, methodology): “From this relationship, we can see that the probability for the race condition is exponentially reduced with the distance from the source voxel as Ps(r ⃗,t) decreases exponentially with distance [4].”
GATE V6: a major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy (2011, methodology): “To date, although several approaches have been described in the literature (Wang et al 1995, Boas et al 2002, Li et al 2004), there is no commonly accepted software to perform such simulations.”
Optical tomography: forward and inverse problems (2009, positive): “For the more complex geometries involved in modelling light propagation in the head, [82, 83] describe 3D voxel-based MC models including anisotropy [84].”
Time domain functional NIRS imaging for human brain mapping (2014, positive): “Nonetheless it was used by several researchers to properly simulate photon migration in realistic adult and neonatal head models (Boas et al., 2002; Fukui et al., 2003).”