ValoMC: a Monte Carlo software and MATLAB toolbox for simulating light transport in biological tissue

Quantitative photoacoustic tomography aims at estimating optical parameters from photoacoustic images that are formed utilizing the photoacoustic effect caused by the absorption of an externally introduced light pulse. This optical parameter estimation is an ill-posed inverse problem, and thus it is sensitive to measurement and modeling errors. In this work, we propose a novel way to solve the inverse problem of quantitative photoacoustic tomography based on the perturbation Monte Carlo method. Monte Carlo method for light propagation is a stochastic approach for simulating photon trajectories in a medium with scattering particles. It is widely accepted as an accurate method to simulate light propagation in tissues. Furthermore, it is numerically robust and easy to implement. Perturbation Monte Carlo maintains this robustness and enables forming gradients for the solution of the inverse problem. We validate the method and apply it in the framework of Bayesian inverse problems. The simulations show that the perturbation Monte Carlo method can be used to estimate spatial distributions of both absorption and scattering parameters simultaneously. These estimates are qualitatively good and quantitatively accurate also in parameter scales that are realistic for biological tissues.

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“…Here we approximate the solution of the RTE with the Monte Carlo method as implemented in ValoMC software and MATLAB toolbox [23].…”

Section: Optical Modelmentioning

confidence: 99%

“…The number of elements and nodes in each mesh is given in Table 1. Photon fluence and absorbed optical energy density were simulated with the Monte Carlo method implemented with ValoMC MATLAB toolbox [23]. The targets were illuminated from the left, right, bottom and top faces separately with a collimated light source (i.e.…”

Section: Data Generationmentioning

confidence: 99%

Perturbation Monte Carlo Method for Quantitative Photoacoustic Tomography

Leino

Lunttila

Mozumder

et al. 2020

IEEE Trans. Med. Imaging

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“…Thus, in QPAT, light propagation can be modeled using a time-independent model of light transport. In this work, both Monte Carlo simulations [43,49,50] and the diffusion approximation [37] are used to model light propagation. The DA is of the form…”

Section: Forward Modelmentioning

confidence: 99%

“…To simulate data, the domain was first illuminated by a planar illumination of uniform intensity covering one side of the rectangular domain. The photon fluence was simulated using a Monte Carlo method [43,49,50] in a piecewise constant triangular discretization H s . Then, the absorbed optical energy density was computed using Equation (2).…”

Section: Data Simulationmentioning

confidence: 99%

Image Reconstruction with Reliability Assessment in Quantitative Photoacoustic Tomography

2018

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Quantitative photoacoustic tomography is a novel imaging method which aims to reconstruct optical parameters of an imaged target based on initial pressure distribution, which can be obtained from ultrasound measurements. In this paper, a method for reconstructing the optical parameters in a Bayesian framework is presented. In addition, evaluating the credibility of the estimates is studied. Furthermore, a Bayesian approximation error method is utilized to compensate the modeling errors caused by coarse discretization of the forward model. The reconstruction method and the reliability of the credibility estimates are investigated with two-dimensional numerical simulations. The results suggest that the Bayesian approach can be used to obtain accurate estimates of the optical parameters and the credibility estimates of these parameters. Furthermore, the Bayesian approximation error method can be used to compensate for the modeling errors caused by a coarse discretization, which can be used to reduce the computational costs of the reconstruction procedure. In addition, taking the modeling errors into account can increase the reliability of the credibility estimates.

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“…In Yan et al, 10 a dual-grid MMC, where a coarsely tessellated mesh and a fine grid are used for raytracing and output storage, respectively, managed to enhance both performance and solution accuracy. Leino et al 11 developed an open-source MMC software which incorporates MATLAB interface for improved usability.…”

Section: Introductionmentioning

confidence: 99%

GPU-accelerated mesh-based Monte Carlo photon transport simulations

Fang

Yan

2019

Preprint

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The mesh-based Monte Carlo (MMC) algorithm is increasingly used as the gold-standard for developing new biophotonics modeling techniques in 3-D complex tissues, including both diffusion-based and various Monte Carlo (MC) based methods. Compared to multi-layered and voxel-based MCs, MMC can utilize tetrahedral meshes to gain improved anatomical accuracy, but also results in higher computational and memory demands. Previous attempts of accelerating MMC using graphics processing units (GPUs) have yielded limited performance improvement and are not publicly available. Here we report a highly efficient MMC -MMCL -using the OpenCL heterogeneous computing framework, and demonstrate a speedup ratio up to 420x compared to state-of-the-art singlethreaded CPU simulations. The MMCL simulator supports almost all advanced features found in our widely disseminated MMC software, such as support for a dozen of complex source forms, wide-field detectors, boundary reflection, photon replay and storing a rich set of detected photon information. Furthermore, this tool supports a wide range of GPUs/CPUs across vendors and is freely available with full source codes and benchmark suites at http://mcx.space/#mmc. c 2019.

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ValoMC: a Monte Carlo software and MATLAB toolbox for simulating light transport in biological tissue

Cited by 79 publications

References 61 publications

Perturbation Monte Carlo Method for Quantitative Photoacoustic Tomography

Perturbation Monte Carlo Method for Quantitative Photoacoustic Tomography

Image Reconstruction with Reliability Assessment in Quantitative Photoacoustic Tomography

GPU-accelerated mesh-based Monte Carlo photon transport simulations

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