DENOISING ON MONTE CARLO DOSE DISTRIBUTIONS

蒙特卡罗剂量分布去噪

• 批准号: 6831637
• 负责人: Joseph O Deasy
• 金额: $ 25.6万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-01-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6831637
• 关键词: computer program /software; cost effectiveness; electromagnetic radiation; head /neck neoplasm; lung neoplasms; mathematical model; method development; neoplasm /cancer radiation therapy; outcomes research; prostate neoplasms; radiation therapy dosage; statistics /biometry

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.

放射疗法治疗计划需要精确的剂量计算，以最大限度地提高精确定向辐射的杀肿瘤效果，同时最大限度地减少输送到附近正常组织的剂量。蒙特卡罗（MC）辐射传输是唯一能够在所有临床感兴趣的情况下满足这一需求的方法。然而，MC剂量计算必须运行，直到统计波动（“噪声”）在所得到的剂量分布得到充分减少。我们已经发现，MC精度可以大大提高，通过统计估计的实际无噪声的潜在剂量分布从嘈杂的模拟输出，一个过程，我们称之为“去噪。“我们已经证明，去噪能够减少MC计算时间至少几倍。我们建议调查应用去噪适形光子治疗和调强放射治疗（IMRT）剂量计算。量化去噪性能的方法将在具体目标#1下开发。MC剂量分布的基准测试套件，包括光子束、IMRT笔形射束和优化的IMRT剂量分布，将在具体目标#2下开发。将在具体目标#3下开发小波收缩阈值去噪。将在具体目标#4下开发使用空间自适应迭代滤波的去噪。两种去噪方法的相对性能和临床可接受性将根据特定目标#1下开发的指标，针对基准测试套件进行测试。在特定目标#5下，我们提出使用离散小波变换来对三维MC生成的笔形射束（PB）剂量分布进行降噪和压缩，并有效地计算IMRT注量加权PB剂量分布。具体目标#6将确定IMRT治疗计划可接受的最大MC PB噪声水平。我们假设外部光子束和IMRT PB的最佳去噪算法将使MC计算时间减少至少5-10倍。我们进一步假设，基于小波的剂量计算方法将：（a）能够使用精确的基于MC的PB剂量分布进行IMRT治疗计划，（B）适用于MC或任何其他PB剂量计算算法，以及（c）在每次IMRT优化迭代时比完全剂量重新计算更有效。这些结果将实现我们的总体目标，提高放射治疗治疗计划的临床效果。