THE CALCULATION OF NONLINEAR RADIATION TRANSPORT BY A MONTE CARLO METHOD.

The Monte Carlo method for radiation transport has been adapted for medical physics application. More specifically, it has received more attention in clinical treatment planning with the development of more efficient computer simulation techniques. In linear accelerator modeling by the Monte Carlo method, the phase space data file (phsp) is an alternative representation for radiation source. However, to create a phase space file and obtain good precision in the results, it is necessary detailed information about the accelerator's head and commonly the supplier does not provide all the necessary data. An alternative to the phsp is the Virtual Source Model (VSM). This alternative approach presents many advantages for the clinical Monte Carlo application. This is the most efficient method for particle generation and can provide an accuracy similar when the phsp is used. This research propose a VSM simulation with the use of a Virtual Flattening Filter (VFF) for profiles and percent depth doses calculation. Two different sizes of open fields (40 x 40 cm² and 40 x 40 cm² rotated 45°) were used and two different source to surface distance (SSD) were applied: the standard 100 cm and custom SSD of 370 cm, which is applied in radiotherapy treatments of total body irradiation. The data generated by the simulation was analyzed and compared with experimental data to validate the VSM. This current model is easy to build and test.

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The Binary Spatial Partitioning Algorithm for Efficient Tracing of Rays in the Monte Carlo Method for Surface-to-Surface Radiation Transport

Heat Transfer, Volume 1 ◽

10.1115/imece2006-13431 ◽

2006 ◽

Author(s):

Sandip Mazumder

Keyword(s):

Monte Carlo ◽

Monte Carlo Method ◽

Binary Tree ◽

Radiation Transport ◽

Three Dimensional ◽

Surface Radiation ◽

Computational Domain ◽

Spatial Partitioning ◽

The Monte Carlo Method ◽

Discrete Surface

The Binary Spatial Partitioning (BSP) algorithm has found prolific usage within the computer graphics community for efficient tracing of rays. In this paper, the BSP algorithm is described and demonstrated in the context of the Monte Carlo method for surface-to-surface radiation transport. In the BSP algorithm the computational domain is recursively bisected into a set of hierarchically linked boxes that are then made use of to narrow down the number of ray-surface intersection calculations. The geometric information pertaining to these hierarchically linked boxes is stored in the form of a binary tree or table. The algorithm is tested for two classical problems, namely an open box, and a box in a box, in both two-dimensional (2D) and three-dimensional (3D) geometries with various mesh sizes, and is found to result in orders of magnitude gains in computational efficiency over direct calculations that do not employ any acceleration strategy. In theory, the BSP algorithm is expected to scale logarithmically, i.e., the CPU time is expected to increase logarithmically with increase in the number of discrete surface elements (or faces) that the boundaries of the computational domain are broken into. In practice, however, it was found that balancing of the binary tree is critical for logarithmic scaling of the algorithm. Without balancing of the binary tree, only super-linear scaling can be attained.

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Mathematical verification of the Monte Carlo maximum cross-section technique

Monte Carlo Methods and Applications ◽

10.1515/mcma-2015-0106 ◽

2015 ◽

Vol 21 (4) ◽

Cited By ~ 2

Author(s):

Victor S. Antyufeev

Keyword(s):

Monte Carlo ◽

Monte Carlo Method ◽

Cross Section ◽

Path Length ◽

Radiation Transport ◽

Free Path Length ◽

Maximum Cross Section ◽

The Monte Carlo Method ◽

Section Technique ◽

Probabilistic Proof

AbstractMaximum cross-section technique is used for solving problems of radiation transport by the Monte Carlo method to optimize the particles' free-path length modeling in inhomogeneous media. A probabilistic proof of a variation of this technique is proposed in the article.

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