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WE‐C‐224C‐04: Correlated Sampling for Accelerating CT‐Based Monte Carlo Dose Calculations for Brachytherapy Treatment Planning
Author(s) -
Le Y,
Todor D,
Williamson J
Publication year - 2006
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 - 10.1118/1.2241709
Subject(s) - voxel , monte carlo method , variance reduction , brachytherapy , nuclear medicine , dosimetry , sampling (signal processing) , materials science , physics , computational physics , mathematics , statistics , computer science , optics , radiation therapy , medicine , radiology , artificial intelligence , detector
Purpose: To investigate the potential of correlated sampling variance reduction technique for accelerating CT‐based Monte Carlo (MC) simulations for calculating 3D brachytherapy dose distributions. Method and Materials: Correlated MC (CMC) simulations generate photon histories in the homogeneous geometry. By recomputing particle weights to account for non‐water composition of the CT voxels and applicator and seed components, a second highly correlated set of histories is constructed, resulting in a lower variance estimate of the dose difference, ΔD=D het ‐D hom To evaluate the accuracy and efficiency of CMC, a clinical permanent prostate implant with 78 I‐125 seeds was simulated using both CMC and UMC versions of our accelerated CT‐based MC dose‐computation code, PTRAN_CT for voxel sizes ranging from 1×1×1mm 3 to 2×2×2mm 3 . Mean efficiency gains were estimated for regions with minimum doses greater than 20%, 50% and 90% of D 90 , as well as different anatomical regions. Results: Systematic differences between UMC and CMC PTRAN_CT were less than 0.4%. Efficiency gains ranged from 4.68 to 15.76 depending on the voxel size and region. CMC can achieve a 2% average precision with 2 mm cubic voxels in 23 seconds on a single P4 processor for voxels with doses > 50%D 90 . Voxels with very low doses and/or large dose perturbations can experience efficiency losses. Because ΔD is a relatively smoothly varying quantity compared to D het , CMC efficiency maybe enhanced using coarser voxel sizes than UMC for the same level of volume‐averaging artifact. Conclusion: Correlated sampling MC can reduce CPU times by an additional 4–15 fold compared to accelerated uncorrelated MC. In practice, larger efficiency enhancements can be achieved because correlated sampling volume‐averaging errors are smaller. MC‐based brachytherapy treatment planning, requiring only a few seconds of CPU time, is achievable. (Supported by NIH‐R01CA46640).