Beamlet dose distribution compression and reconstruction using wavelets for intensity modulated treatment planning.
Beamlet dose distribution compression and reconstruction using wavelets for intensity modulated treatment planning.
复制标题
使用小波进行小波束剂量分布压缩和重建以进行强度调制治疗计划。
DOI：
10.1118/1.1636560
复制
发表时间：
2004
期刊：
影响因子：
3.8
通讯作者：
Deasy,JosephO
中科院分区：
文献类型：
--
作者：
Zakarian,Constantine;Deasy,JosephO
调强放射治疗（IMRT）治疗计划通常被表述为固定几何形状子场（子射束）权重的优化。有效的优化技术可以基于将子射束权重与剂量值相关联的影响矩阵的直接存储。然而，直接存储用于IMRT治疗计划的小射束剂量分布可以容易地超过几千兆字节，并且因此通常是不可行的。我们提出了一种快速计算全三维调强放射治疗剂量分布的方法，基于一个矢量的子束权重。该方法基于使用快速数字小波变换和所谓的硬阈值的压缩子束剂量分布。我们使用来自在均匀（水）和非均匀（CT数据）体模中模拟的单能6 MeV光子点源的横截面矩形子束研究了该方法。使用精确的VMC++ Monte Carlo引擎计算剂量。通过将小波系数降低到给定阈值以下来对小射束剂量分布进行小波变换和压缩。然后使用剩余的小波计算剂量。选择小波基函数，分解水平，和阈值，为不同的切片方向（横向或平行于光束）和不同角度的小光束入射进行了研究。使用sym2小波和0.01的阈值，包含小射束的平面的典型切片内压缩比为32：1，对于高于最大剂量50%的体素，典型均方根误差约为0.04%。与全矩阵存储相比，包括具有很少信息内容的许多平面的整体压缩性能在100：1或更大的量级上。虽然其他方法可以使存储的影响矩阵值的使用更可行的IMRT治疗计划（如使用粗网格或限制值定义的体积的兴趣），我们得出结论，小波压缩有利于存储和使用完整的笔剂量沉积（影响矩阵）数据在IMRT治疗计划。
Intensity modulated radiation therapy (IMRT) treatment planning is often formulated as the optimization of weights of fixed‐geometry subfields (beamlets). Efficient optimization techniques can be based on direct storage of the influence matrix relating beamlet weights to dose values. However, direct storage of beamlet dose distributions for IMRT treatment planning can easily exceed several gigabytes, and is therefore often not feasible. We present a method for rapidly calculating full three‐dimensional IMRT dose distributions, based on a vector of beamlet weights. The method is based on compressed beamlet dose distributions using fast digital wavelet transforms and so‐called hard thresholding. We studied the method with a rectangular beamlet of cross section from a monoenergetic 6 MeV photon point source simulated in homogeneous (water) and heterogeneous (CT‐data) phantoms. Dose was calculated using the accurate VMC++ Monte Carlo engine. The beamlet dose distributions were wavelet transformed and compressed by dropping wavelet coefficients below a given threshold value. Dose is then computed using the remaining wavelets. Selection of the wavelet basis function, decomposition level, and threshold values, for different slice orientations (transverse or parallel to the beam) and varying angles of beamlet incidence are studied. A typical in‐slice compression ratio for a plane containing a beamlet was 32:1 using thesym2wavelet and a threshold of 0.01, with a typical root‐mean‐square error, for voxels above 50% of the maximum dose, of about 0.04%. The overall compression performance, which includes many planes with little information content, is on the order of 100:1 or greater compared to full matrix storage. Although other methods are available to make the use of stored influence matrix values more feasible in IMRT treatment planning (such as using coarse grids or restricting values to defined volumes of interest) we conclude that wavelet compression facilitates the storage and use of full pencil dose deposition (influence matrix) data in IMRT treatment planning.