scholarly journals Light transport modeling in highly complex tissues using the implicit mesh-based Monte Carlo algorithm

2020 ◽  
Vol 12 (1) ◽  
pp. 147
Author(s):  
Yaoshen Yuan ◽  
Shijie Yan ◽  
Qianqian Fang
Keyword(s):  
Monte Carlo ◽  
Light Transport ◽  
Transport Modeling ◽  
Monte Carlo Algorithm

Parallel Simulation of Steady State Light Transport Through Solid Media Using Logistic Map

2020 ◽  
Vol 17 (4) ◽  
pp. 1606-1609
Author(s):  
K. Sathish ◽  
Siddharth Singh ◽  
Anirudh Agarwal ◽  
Pranab Kumar Shukla
Keyword(s):  
Monte Carlo ◽  
Photodynamic Therapy ◽  
Gpu Computing ◽  
Logistic Map ◽  
Parallel Simulation ◽  
Light Transport ◽  
Monte Carlo Algorithm ◽  
Photon Transport ◽  
Solid Media ◽  
Wide Range

The Monte Carlo algorithm has been extensively used for photon transport simulations in medical imaging to assists doctors in Photodynamic Therapy for treatment of wide range of medical conditions including varieties of cancer by eliciting phototoxicity in cells. Previously this was done using static 2 Dimensional models on traditional CPUs. With the advent of GPU Computing further work was done to extend this model by separating the PRNG.

scholarly journals MCMLpar and MCSLinv: A Parallel Version of MCML and an Inverse Monte Carlo Algorithm to Calculate Optical Scattering Parameters

2017 ◽  
Vol 122 ◽  
Author(s):  
Richelle H. Streater ◽  
Anne-Michelle R. Lieberson ◽  
Adam L. Pintar ◽  
Zachary H. Levine
Keyword(s):  
Monte Carlo ◽  
Quadratic Function ◽  
Search Space ◽  
Monte Carlo Sampling ◽  
Optical Scattering ◽  
Light Transport ◽  
Monte Carlo Algorithm ◽  
Optimum Parameters ◽  
Inverse Monte Carlo ◽  
Log Likelihood

The MCML program for Monte Carlo modeling of light transport in multi-layered tissues has been widely used in the past 20 years or so. Here, we have re-implemented MCML for solving the inverse problem. Our formulation features optimizing the profile log likelihood which takes into account uncertainties due to both experimental and Monte Carlo sampling. We limit the search space for the optimum parameters with relatively few Monte Carlo trials and then iteratively double the number of Monte Carlo trials until the search space stabilizes. At this point, the log likelihood can be fit with a quadratic function to find the optimum. The time-to-solution is only a few minutes in typical cases because we use importance sampling to determine the log likelihood on a grid of parameters at each iteration. Also, our implementation uses OpenMP and SPRNG to generate Monte Carlo trials in parallel.

scholarly journals Accelerating Monte Carlo modeling of structured-light based diffuse optical imaging via “photon sharing”

2020 ◽  
Author(s):  
Shijie Yan ◽  
Ruoyang Yao ◽  
Xavier Intes ◽  
Qianqian Fang
Keyword(s):  
Monte Carlo ◽  
Inverse Problems ◽  
Light Transport ◽  
Transport Modeling ◽  
Imaging Systems ◽  
Computationally Efficient ◽  
Monte Carlo Modeling ◽  
Detection Techniques ◽  
Modulated Imaging ◽  
Single Pixel

The increasing use of spatially-modulated imaging and single-pixel detection techniques demands computationally efficient methods for light transport modeling. Herein, we report an easy-to-implement yet significantly more efficient Monte Carlo (MC) method for simultaneously simulating spatially modulated illumination and detection patterns accurately in 3-D complex domains. We have implemented this accelerated algorithm, named “photon sharing”, in our open-source MC simulators, reporting 13.6× and 5.5× speedups in mesh- and voxel-based MC benchmarks, respectively. In addition, the proposed algorithm is readily used for accelerating the solving of inverse problems in spatially-modulated imaging systems by building Jaco-bians of all illumination-detection pattern pairs concurrently, resulting in a 12.4-fold speed improvement.https://doi.org/10.1101/2020.02.16.951590

scholarly journals A hybrid mesh and voxel based Monte Carlo algorithm for accurate and efficient photon transport modeling in complex bio-tissues

2020 ◽  
Author(s):  
Shijie Yan ◽  
Qianqian Fang
Keyword(s):  
Monte Carlo ◽  
Graphics Processing Units ◽  
Light Transport ◽  
Monte Carlo Algorithm ◽  
Brain Atlas ◽  
Simulation Accuracy ◽  
Mc Simulation ◽  
Voxel Data ◽  
Graphics Processing ◽  
Improved Accuracy

AbstractOver the past decade, an increasing body of evidence has suggested that threedimensional (3-D) Monte Carlo (MC) light transport simulations are affected by the inherent limitations and errors of voxel-based domain boundaries. In this work, we specifically address this challenge using a hybrid MC algorithm, namely split-voxel MC or SVMC, that combines both mesh and voxel domain information to greatly improve MC simulation accuracy while remaining highly flexible and efficient in parallel hardware, such as graphics processing units (GPU). We achieve this by applying a marching-cubes algorithm to a pre-segmented domain to extract and encode sub-voxel information of curved surfaces, which is then used to inform ray-tracing computation within boundary voxels. This preservation of curved boundaries in a voxel data structure demonstrates significantly improved accuracy in several benchmarks, including a human brain atlas. The accuracy of the SVMC algorithm is comparable to that of mesh-based MC (MMC), but runs 2x-6x faster and requires only a lightweight preprocessing step. The proposed algorithm has been implemented in our open-source software and is freely available at http://mcx.space.

scholarly journals Light transport modeling in highly complex tissues using implicit mesh-based Monte Carlo algorithm

2020 ◽  
Author(s):  
Yaoshen Yuan ◽  
Shijie Yan ◽  
Qianqian Fang
Keyword(s):  
Monte Carlo ◽  
Light Propagation ◽  
Light Transport ◽  
Monte Carlo Algorithm ◽  
Memory Usage ◽  
Microvascular Networks ◽  
Multi Scale ◽  
Tissue Structures ◽  
Vessel Networks ◽  
Shape Complexity

AbstractThe mesh-based Monte Carlo (MMC) technique has grown tremendously since its initial publication nearly a decade ago. It is now recognized as one of the most accurate Monte Carlo (MC) methods, providing accurate reference solutions for the development of novel biophotonics techniques. In this work, we aim to further advance MMC to address a major challenge in biophotonics modeling, i.e. light transport within highly complex tissues, such as dense microvascular networks, porous media and multi-scale tissue structures. Although the current MMC framework is capable of simulating light propagation in such media given its generality, the run-time and memory usage grow rapidly with increasing media complexity and size. This greatly limits our capability to explore complex and multi-scale tissue structures. Here, we propose a highly efficient implicit mesh-based Monte Carlo (iMMC) method that incorporates both mesh- and shape-based tissue representations to create highly complex yet memory efficient light transport simulations. We demonstrate that iMMC is capable of providing accurate solutions for dense vessel networks and porous tissues while reducing memory usage by greater than a hundred- or even thousand-fold. In a sample network of microvasculature, the reduced shape complexity results in nearly 3x speed acceleration. The proposed algorithm is now available in our open-source MMC software at http://mcx.space/#mmc.

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