Díaz Londoño G, García-Pareja S, Salvat F, Lallena AM. Simple variance reduction in Monte Carlo calculations of specific absorbed fractions: Russian roulette and splitting at the source organ.
Biomed Phys Eng Express 2020;
6:035015. [PMID:
33438660 DOI:
10.1088/2057-1976/ab817f]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
PURPOSE
To investigate the capabilities of several variance reduction techniques in the calculation of specific absorbed fractions in cases where the source and the target organs are far away and/or the target organs have a small volume.
METHODS
The specific absorbed fractions have been calculated by using the Monte Carlo code PENELOPE and by assuming the thyroid gland as the source organ and the testicles, the urinary bladder, the uterus, and the ovaries as the target ones. A mathematical anthropomorphic phantom, similar to the MIRD-type phantoms, has been considered. Photons with initial energies of 50, 100 and 500 keV were emitted isotropically from the volume of the source organ. Simulations have been carried out by implementing the variance reduction techniques of splitting and Russian roulette at the source organ only and the interaction forcing at the target organs. The influence of the implementation details of those techniques have been investigated and optimal parameters have been determined. All simulations were run with a CPU time of 1.5 · 105 s.
RESULTS
Specific absorbed fractions with relative uncertainties well below 10% have been obtained in most cases, agreeing with those used as reference. The best value for the factor defining the application of the Russian roulette technique was r = 0.3. The best value for the splitting number was between s = 3 and s = 10, depending on the specific energies and target organs.
CONCLUSIONS
The proposed strategy provides an effective method for computing specific absorbed fractions for the most unfavorable situations, with a computing effort that is considerably reduced with respect to other methodologies.
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