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 A FOURTH ORDER DIFFUSION MONTE CARLO ALGORITHM FOR SOLVING QUANTUM MANY-BODY PROBLEMS

2001 ◽  
Vol 15 (10n11) ◽  
pp. 1752-1755 ◽  
Author(s):  
H. A. FORBERT ◽  
S. A. CHIN
Keyword(s):  
Monte Carlo ◽  
Fourth Order ◽  
Planck Equation ◽  
Monte Carlo Algorithm ◽  
Bulk Liquid ◽  
Many Body ◽  
Diffusion Monte Carlo ◽  
Step Size ◽  
Wide Range ◽  
Gaussian Random Walk

We derive a fourth-order diffusion Monte Carlo algorithm for solving quantum many-body problems. The method uses a factorization of the imaginary time propagator in terms of the usual local energy E and Langevin operators L as well as an additional pseudo-potential consisting of the double commutator [EL, [L, EL]]. A new factorization of the propagator of the Fokker-Planck equation enables us to implement the Langevin algorithm to the necessary fourth order. We achieve this by the addition of correction terms to the drift steps and the use of a position-dependent Gaussian random walk. We show that in the case of bulk liquid helium the systematic step size errors are indeed fourth order over a wide range of step sizes.

scholarly journals A fast, primary-interaction Monte Carlo methodology for determination of total efficiency of cylindrical scintillation gamma-ray detectors

2009 ◽  
Vol 24 (3) ◽  
pp. 195-203 ◽  
Author(s):  
Shakeel Rehman ◽  
Sikander Mirza ◽  
Nasir Mirza
Keyword(s):  
Monte Carlo ◽  
Point Sources ◽  
Monte Carlo Algorithm ◽  
Total Efficiency ◽  
Axial Distance ◽  
Detector Efficiency ◽  
Wide Range ◽  
Energy Dependent ◽  
Primary Interaction

A primary-interaction based Monte Carlo algorithm has been developed for determination of the total efficiency of cylindrical scintillation g-ray detectors. This methodology has been implemented in a Matlab based computer program BPIMC. For point isotropic sources at axial locations with respect to the detector axis, excellent agreement has been found between the predictions of the BPIMC code with the corresponding results obtained by using hybrid Monte Carlo as well as by experimental measurements over a wide range of g-ray energy values. For off-axis located point sources, the comparison of the BPIMC predictions with the corresponding results obtained by direct calculations as well as by conventional Monte Carlo schemes shows good agreement validating the proposed algorithm. Using the BPIMC program, the energy dependent detector efficiency has been found to approach an asymptotic profile by increasing either thickness or diameter of scintillator while keeping the other fixed. The variation of energy dependent total efficiency of a 3'x3' NaI(Tl) scintillator with axial distance has been studied using the BPIMC code. About two orders of magnitude change in detector efficiency has been observed for zero to 50 cm variation in the axial distance. For small values of axial separation, a similar large variation has also been observed in total efficiency for 137Cs as well as for 60Co sources by increasing the axial-offset from zero to 50 cm.

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.

A Matlab-based Monte Carlo algorithm for transport of gamma-rays in matter

2015 ◽  
Vol 21 (1) ◽  
Author(s):  
Mohsen Sharifzadeh ◽  
Hosein Afarideh ◽  
Hosein Khalafi ◽  
Reza Gholipour
Keyword(s):  
Monte Carlo ◽  
Treatment Planning ◽  
Cross Section ◽  
Gamma Rays ◽  
Radiation Detection ◽  
Monte Carlo Algorithm ◽  
Photon Transport ◽  
Future Work ◽  
Visible Photon ◽  
Good Agreement

AbstractTransport of gamma photons in many fields such as radiation detection, dosimetry, and treatment planning is used. The transport of 1 MeV and 5 MeV photons in a water phantom was simulated by using the Monte Carlo algorithm in Matlab work space. The result of KERMA calculation showed good agreement with Arqueros and Montesinos [American Journal of Physics 71 (2003), no. 1, 38–45], and MCNP4C. The proposed algorithm has ability to read out from different cross section libraries. As the future work, it also can be extend for the electron and visible photon transport.

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.

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