Radiance Monte-Carlo for application of the radiative transport equation in the inverse problem of diffuse optical tomography

Powell, Samuel; Hochuli, Roman; Arridge, Simon

doi:10.1117/12.2250005

Cited by 3 publications

(11 citation statements)

References 19 publications

(1 reference statement)

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“…2.2. The approximation of the radiance field as a finite sum of spherical harmonics 31 within an MC light transport model is described in Secs. 2.3 and 2.4.…”

Section: Methodsmentioning

confidence: 99%

“…As the radiance ϕðsÞ and the adjoint radiance ϕ Ã ðsÞ are functions of solid angle and are defined on the surface of the unit sphere, both quantities can be expressed on the basis of spherical harmonic functions. The expansion of the radiance into spherical harmonics is based on previous work 31 and is inspired by the P n approximations, described in Ref. 22 (similar to a Fourier expansion in 1-D or 2-D) and is outlined in detail in Sec.…”

Section: Radiance Approximationmentioning

confidence: 99%

“…To satisfy Eq. (12), a radiance MC algorithm (RMC) 19,31 is used. To obtain the radiance ϕðrÞ, the directional information of the photon traversing a voxel at positionr is stored by depositing the photon weight into the relevant spherical harmonics coefficients ψ lm ðrÞ.…”

Section: Radiance Monte Carlo Simulationsmentioning

confidence: 99%

“…The RMC code is implemented in the Julia programming language, 38 which controls and dispatches the execution of kernels on both the CPU (written in Julia), and the GPU (written in NVIDIA's CUDA language). 31 Because the definition of the adjoint model does not change the dynamics of photon propagation, the same RMC simulation code provides the adjoint radiance ϕ Ã ðrÞ.…”

Section: Radiance Monte Carlo Simulationsmentioning

confidence: 99%

“…23 Methods that aim to solve the radiative transfer equation (RTE) directly are computationally expensive and have only been demonstrated in twodimensional (2-D) images so far. [24][25][26][27] Monte Carlo (MC) models have recently gained attention [28][29][30][31] due to their highly parallelized architecture and advances in graphics-processing units and have already been applied to 2-D QPAT inversions 19 and in initial studies with limited parameter space in 3-D. 16,32 In this paper, a method for inverting multiwavelength 3-D images based on an adjoint formulation of a radiance MC model is demonstrated in silico. The challenges that are addressed in this work are (1) the optimization of the objective function using inherently noisy gradients, (2) accounting for the effect of the concentration-dependent Grüneisen parameter, and (3) the representation of the radiance in terms of spherical harmonics.…”

Section: Introductionmentioning

confidence: 99%

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Three-dimensional quantitative photoacoustic tomography using an adjoint radiance Monte Carlo model and gradient descent

et al. 2019

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Quantitative photoacoustic tomography aims to recover maps of the local concentrations of tissue chromophores from multispectral images. While model-based inversion schemes are promising approaches, major challenges to their practical implementation include the unknown fluence distribution and the scale of the inverse problem. We describe an inversion scheme based on a radiance Monte Carlo model and an adjoint-assisted gradient optimization that incorporates fluence-dependent step sizes and adaptive moment estimation. The inversion is shown to recover absolute chromophore concentrations, blood oxygen saturation, and the Grüneisen parameter from in silico three-dimensional phantom images for different radiance approximations. The scattering coefficient is assumed to be homogeneous and known a priori.

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“…2.2. The approximation of the radiance field as a finite sum of spherical harmonics 31 within an MC light transport model is described in Secs. 2.3 and 2.4.…”

Section: Methodsmentioning

confidence: 99%

Section: Radiance Approximationmentioning

confidence: 99%

Section: Radiance Monte Carlo Simulationsmentioning

confidence: 99%

Section: Radiance Monte Carlo Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Three-dimensional quantitative photoacoustic tomography using an adjoint radiance Monte Carlo model and gradient descent

et al. 2019

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Differential photon waves imaging

Kazancı

2020

Int J Imaging Syst Tech

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Imaging philosophy was simulated for well‐known biomedical diffuse optical imaging (DOI) methodology. It runs based on the differential photon waves. The underlying philosophy of imaging methodology takes its origin from the distance of the same molecule types. Inherently, each type of biological molecules has different molecular structure, size, and binding distance. Molecules might be water, oxy‐hemoglobin, deoxyhemoglobin, ATP, pepsin, cholesterol, protein, fats or lipids, hydroxyapatite, carbohydrates, glucose, glycogen, DNA, etc. These molecules need to be grouped for biomedical photon imaging purposes such as in hair, skull, scalp, cortex, dermis, epidermis, cerebrospinal fluid, cerebellar gray and white matter for neuroimaging; or skin, fat, water for breast imaging; optic nerve, humor aqueous for eye imaging; bones and joints for skeletal system imaging; liver, muscle, fibroadenoma breast, mucous tissue, nodule, bowel, heart, kidneys, lung, stomach, whole blood vascular system for inner organs. Among these different types of molecular structures, each type has different characteristic photon absorption and scattering coefficients for corresponding wavelength. Researchers have been using these characteristic wavelengths to reconstruct the images. They have focused on either absorption or scattering maximums of specific wavelengths for each tissue type. This is right when the molecules are investigated first, but it is a dead‐end when it goes deep tissue, since low‐power laser photons scatter randomly when they penetrate inside the tissue, immediately. Novel approach uses phase‐shifted group photon waves corresponding to the molecular distances. Single photon wavelength can be used to investigate the molecules inside the imaging media.

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Broadband Time Domain Diffuse Optical Reflectance Spectroscopy: A Review of Systems, Methods, and Applications

et al. 2019

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This review presents recent developments and a wide overview of broadband time domain diffuse optical spectroscopy (TD-DOS). Various topics including physics of photon migration, advanced instrumentation, methods of analysis, applications covering multiple domains (tissue chromophore, in vivo studies, food, wood, pharmaceutical industry) are elaborated. The key role of standardization and recent studies in that direction are discussed. Towards the end, a brief outlook is presented on the current status and future trends in broadband TD-DOS.

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Radiance Monte-Carlo for application of the radiative transport equation in the inverse problem of diffuse optical tomography

Cited by 3 publications

References 19 publications

Three-dimensional quantitative photoacoustic tomography using an adjoint radiance Monte Carlo model and gradient descent

Three-dimensional quantitative photoacoustic tomography using an adjoint radiance Monte Carlo model and gradient descent

Differential photon waves imaging

Broadband Time Domain Diffuse Optical Reflectance Spectroscopy: A Review of Systems, Methods, and Applications

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