Radiation therapy treatment planning requires accurate dose calculations in order to maximize the tumoricidal effects of precisely directed radiation while minimizing doses delivered to nearby normal tissues. Monte Carlo (MC) radiation transport is the only method which is capable of fulfilling this need in all situations of clinical interest. However, MC dose computations must be run until statistical fluctuations (""""""""noise"""""""") in the resulting dose distributions are adequately reduced. We have discovered that MC precision can be greatly improved through statistical estimation of the actual noise-free underlying dose distribution from the noisy simulation output, a process we term """"""""denoising."""""""" We have shown that denoising is capable of reducing MC calculation times at least several-fold. We propose to investigate the application of denoising to conformal photon therapy and intensity modulated radiation therapy (IMRT) dose calculations. Metrics for quantifying denoising performance will be developed under Specific Aim #1. A benchmark test suite of MC dose distributions, including photon beam, IMRT pencil beam, and optimized IMRT dose distributions, will be developed under Specific Aim #2. Wavelet shrinkage threshold denoising will be developed under Specific Aim #3. Denoising using spatially adaptive iterative filtering will be developed under Specific Aim #4. The relative performance and clinical acceptability of the two denoising methods will be tested against the benchmark test suite with the metrics developed under Specific Aim #1.
Under Specific Aim #5 we propose to use discrete wavelet transforms to denoise and compress three dimensional MC- generated pencil beam (PB) dose distributions, and to efficiently compute IMRT fluence-weighted PB dose distributions.
Specific Aim #6 will establish maximum MC PB noise levels acceptable for IMRT treatment planning. We hypothesize that optimal denoising algorithms for external photon beams and IMRT PBs will decrease MC computation times by at least a factor of 5-10. We further hypothesize that wavelet-based dose computation methods will: (a) enable use of accurate MC-based PB dose distributions for IMRT treatment planning, (b) apply to MC or any other PB dose calculation algorithm, and (c) be far more computationally efficient than complete dose recalculations at each IMRT optimization iteration. These results would achieve our overall goal of increasing the clinical effectiveness of radiation therapy treatment planning.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA090445-03
Application #
6692678
Study Section
Radiation Study Section (RAD)
Program Officer
Stone, Helen B
Project Start
2002-01-01
Project End
2005-12-31
Budget Start
2004-01-01
Budget End
2004-12-31
Support Year
3
Fiscal Year
2004
Total Cost
$256,025
Indirect Cost
Name
Washington University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
Davidson, Scott E; Cui, Jing; Kry, Stephen et al. (2016) Modification and validation of an analytical source model for external beam radiotherapy Monte Carlo dose calculations. Med Phys 43:4842
Clark, V H; Chen, Y; Wilkens, J et al. (2008) IMRT treatment planning for prostate cancer using prioritized prescription optimization and mean-tail-dose functions. Linear Algebra Appl 428:1345-1364
Lindsay, Patricia E; El Naqa, Issam; Hope, Andrew J et al. (2007) Retrospective monte carlo dose calculations with limited beam weight information. Med Phys 34:334-46
El Naqa, I; Cui, J; Lindsay, P et al. (2007) The denoising of Monte Carlo dose distributions using convolution superposition calculations. Phys Med Biol 52:N375-85
Wilkens, Jan J; Alaly, James R; Zakarian, Konstantin et al. (2007) IMRT treatment planning based on prioritizing prescription goals. Phys Med Biol 52:1675-92
Deasy, Joseph O; Alaly, James R; Zakaryan, Konstantin (2007) Obstacles and advances in intensity-modulated radiation therapy treatment planning. Front Radiat Ther Oncol 40:42-58
El Naqa, I; Kawrakow, I; Fippel, M et al. (2005) A comparison of Monte Carlo dose calculation denoising techniques. Phys Med Biol 50:909-22
Zakarian, Constantine; Deasy, Joseph O (2004) Beamlet dose distribution compression and reconstruction using wavelets for intensity modulated treatment planning. Med Phys 31:368-75