Hünemohr Nora, Paganetti Harald, Greilich Steffen, Jäkel Oliver, Seco Joao
Medical Physics in Radiation Oncology, German Cancer Research Center, 69120 Heidelberg, Germany.
Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114.
Med Phys. 2014 Jun;41(6):061714. doi: 10.1118/1.4875976.
The authors describe a novel method of predicting mass density and elemental mass fractions of tissues from dual energy CT (DECT) data for Monte Carlo (MC) based dose planning.
The relative electron density ϱ(e) and effective atomic number Z(eff) are calculated for 71 tabulated tissue compositions. For MC simulations, the mass density is derived via one linear fit in the ϱ(e) that covers the entire range of tissue compositions (except lung tissue). Elemental mass fractions are predicted from the ϱ(e) and the Z(eff) in combination. Since particle therapy dose planning and verification is especially sensitive to accurate material assignment, differences to the ground truth are further analyzed for mass density, I-value predictions, and stopping power ratios (SPR) for ions. Dose studies with monoenergetic proton and carbon ions in 12 tissues which showed the largest differences of single energy CT (SECT) to DECT are presented with respect to range uncertainties. The standard approach (SECT) and the new DECT approach are compared to reference Bragg peak positions.
Mean deviations to ground truth in mass density predictions could be reduced for soft tissue from (0.5±0.6)% (SECT) to (0.2±0.2)% with the DECT method. Maximum SPR deviations could be reduced significantly for soft tissue from 3.1% (SECT) to 0.7% (DECT) and for bone tissue from 0.8% to 0.1%. Mean I-value deviations could be reduced for soft tissue from (1.1±1.4%, SECT) to (0.4±0.3%) with the presented method. Predictions of elemental composition were improved for every element. Mean and maximum deviations from ground truth of all elemental mass fractions could be reduced by at least a half with DECT compared to SECT (except soft tissue hydrogen and nitrogen where the reduction was slightly smaller). The carbon and oxygen mass fraction predictions profit especially from the DECT information. Dose studies showed that most of the 12 selected tissues would profit significantly (up to 2.2%) from DECT material decomposition with no noise present. The ϱ(e) associated with an absolute noise of ±0.01 and Z(eff) associated with an absolute noise of ±0.2 resulted in ±10% standard variation in the carbon and oxygen mass fraction prediction.
Accurate stopping power prediction is mainly determined by the correct mass density prediction. Theoretical improvements in range predictions with DECT data in the order of 0.1%-2.1% were observed. Further work is needed to quantify the potential improvements from DECT compared to SECT in measured image data associated with artifacts and noise.
作者描述了一种从双能CT（DECT）数据预测组织质量密度和元素质量分数的新方法，用于基于蒙特卡罗（MC）的剂量规划。
针对71种列表组织成分计算相对电子密度ϱ(e)和有效原子序数Z(eff)。对于MC模拟，通过在涵盖整个组织成分范围（肺组织除外）的ϱ(e)中进行一次线性拟合来推导质量密度。结合ϱ(e)和Z(eff)来预测元素质量分数。由于粒子治疗剂量规划和验证对准确的材料赋值特别敏感，因此进一步分析了质量密度、I值预测以及离子阻止本领比（SPR）与真实值的差异。给出了在12种组织中进行的单能质子和碳离子剂量研究，这些组织在单能CT（SECT）与DECT之间显示出最大差异，涉及射程不确定性。将标准方法（SECT）和新的DECT方法与参考布拉格峰位置进行比较。
使用DECT方法，软组织质量密度预测中与真实值的平均偏差可从（0.5±0.6）%（SECT）降低至（0.2±0.2）%。软组织的最大SPR偏差可从3.1%（SECT）显著降低至0.7%（DECT），骨组织的最大SPR偏差可从0.8%降低至0.1%。使用本方法，软组织的平均I值偏差可从（1.1±1.4%，SECT）降低至（0.4±0.3）%。每种元素的元素组成预测都得到了改善。与SECT相比，DECT可将所有元素质量分数与真实值的平均和最大偏差至少降低一半（软组织中的氢和氮除外，其降低幅度略小）。碳和氧质量分数预测尤其受益于DECT信息。剂量研究表明，在不存在噪声的情况下，所选的12种组织中的大多数将从DECT材料分解中显著受益（高达2.2%）。与绝对噪声±0.01相关的ϱ(e)和与绝对噪声±0.2相关的Z(eff)导致碳和氧质量分数预测的标准变化为±10%。
准确的阻止本领预测主要取决于正确的质量密度预测。观察到使用DECT数据在射程预测方面的理论改进幅度为0.1% - 2.1%。需要进一步开展工作来量化与SECT相比，DECT在与伪影和噪声相关的测量图像数据中的潜在改进。
