Radiation Physics, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
Med Phys. 2017 Jun;44(6):2345-2357. doi: 10.1002/mp.12238. Epub 2017 May 4.
To develop and evaluate-in a proof-of-concept configuration-a novel iterative reconstruction algorithm (DIRA) for quantitative determination of elemental composition of patient tissues for application to brachytherapy with low energy (< 50 keV) photons and proton therapy.
DIRA was designed as a model-based iterative reconstruction algorithm, which uses filtered backprojection, automatic segmentation and multimaterial tissue decomposition. The evaluation was done for a phantom derived from the voxelized ICRP 110 male phantom. Soft tissues were decomposed to the lipid, protein and water triplet, bones were decomposed to the compact bone and bone marrow doublet. Projections were derived using the Drasim simulation code for an axial scanning configuration resembling a typical DECT (dual-energy CT) scanner with 80 kV and Sn140 kV x-ray spectra. The iterative loop produced mono-energetic images at 50 and 88 keV without beam hardening artifacts. Different noise levels were considered: no noise, a typical noise level in diagnostic imaging and reduced noise level corresponding to tenfold higher doses. An uncertainty analysis of the results was performed using type A and B evaluations. The two approaches were compared.
Linear attenuation coefficients averaged over a region were obtained with relative errors less than 0.5% for all evaluated regions. Errors in average mass fractions of the three-material decomposition were less than 0.04 for no noise and reduced noise levels and less than 0.11 for the typical noise level. Mass fractions of individual pixels were strongly affected by noise, which slightly increased after the first iteration but subsequently stabilized. Estimates of uncertainties in mass fractions provided by the type B evaluation differed from the type A estimates by less than 1.5% for most cases. The algorithm was fast, the results converged after 5 iterations. The algorithmic complexity of forward polyenergetic projection calculation was much reduced by using material doublets and triplets.
The simulations indicated that DIRA is capable of determining elemental composition of tissues, which are needed in brachytherapy with low energy (< 50 keV) photons and proton therapy. The algorithm provided quantitative monoenergetic images with beam hardening artifacts removed. Its convergence was fast, image sharpness expressed via the modulation transfer function was maintained, and image noise did not increase with the number of iterations.
开发并评估一种新颖的迭代重建算法（DIRA），用于定量测定患者组织的元素组成，应用于低能（<50keV）光子和质子治疗的近距离放射治疗。
DIRA 被设计为一种基于模型的迭代重建算法，它使用滤波反投影、自动分割和多物质组织分解。评估是针对源自体素化 ICRP 110 男性体模的体模进行的。软组织分解为脂质、蛋白质和水三重，骨骼分解为密质骨和骨髓双重。使用 Drasim 模拟代码生成投影，该代码模拟了类似于典型的 DECT（双能 CT）扫描仪的轴向扫描配置，使用 80kV 和 Sn140kV X 射线光谱。迭代循环在没有束硬化伪影的情况下产生 50keV 和 88keV 的单能图像。考虑了不同的噪声水平：无噪声、诊断成像中的典型噪声水平和对应于十倍高剂量的降低噪声水平。使用 A 型和 B 型评估对结果进行了不确定性分析。比较了这两种方法。
在所有评估区域中，平均区域的线性衰减系数的相对误差小于 0.5%。对于无噪声和降低噪声水平，三物质分解的平均质量分数误差小于 0.04，对于典型噪声水平，误差小于 0.11。个别像素的质量分数受噪声的强烈影响，在第一次迭代后略有增加，但随后稳定下来。B 型评估提供的质量分数不确定度估计与 A 型估计的差异小于 1.5%，大多数情况下。该算法速度很快，经过 5 次迭代后结果收敛。通过使用物质对偶和三重，向前多能投影计算的算法复杂度大大降低。
模拟表明，DIRA 能够确定低能（<50keV）光子和质子治疗近距离放射治疗所需的组织元素组成。该算法提供了去除束硬化伪影的定量单能图像。它的收敛速度很快，调制传递函数表示的图像锐度得以保持，并且图像噪声不会随迭代次数的增加而增加。
