Premium
Accelerating Monte Carlo simulations of radiation therapy dose distributions using wavelet threshold de‐noising
Author(s) -
Deasy Joseph O.,
Wickerhauser M. Victor,
Picard Mathieu
Publication year - 2002
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1508112
Subject(s) - monte carlo method , wavelet , imaging phantom , wavelet transform , physics , algorithm , mathematics , computer science , artificial intelligence , optics , statistics
The Monte Carlo dose calculation method works by simulating individual energetic photons or electrons as they traverse a digital representation of the patient anatomy. However, Monte Carlo results fluctuate until a large number of particles are simulated. We propose wavelet threshold de‐noising as a postprocessing step to accelerate convergence of Monte Carlo dose calculations. A sampled rough function (such as Monte Carlo noise) gives wavelet transform coefficients which are more nearly equal in amplitude than those of a sampled smooth function. Wavelet hard‐threshold de‐noising sets to zero those wavelet coefficients which fall below a threshold; the image is then reconstructed. We implemented the computationally efficient 9,7‐biorthogonal filters in the C language. Transform results were averaged over transform origin selections to reduce artifacts. A method for selecting best threshold values is described. The algorithm requires about 336 floating point arithmetic operations per dose grid point. We applied wavelet threshold de‐noising to two two‐dimensional dose distributions: a dose distribution generated by 10 MeV electrons incident on a water phantom with a step‐heterogeneity, and a slice from a lung heterogeneity phantom. Dose distributions were simulated using the Integrated Tiger Series Monte Carlo code. We studied threshold selection, resulting dose image smoothness, and resulting dose image accuracy as a function of the number of source particles. For both phantoms, with a suitable value of the threshold parameter, voxel‐to‐voxel noise was suppressed with little introduction of bias. The roughness of wavelet de‐noised dose distributions (according to a Laplacian metric) was nearly independent of the number of source electrons, though the accuracy of the de‐noised dose image improved with increasing numbers of source electrons. We conclude that wavelet shrinkage de‐noising is a promising method for effectively accelerating Monte Carlo dose calculations by factors of 2 or more.