Department of Radiation Sciences, Radiation Physics, Umeå University, Umeå, Sweden.
Med Phys. 2021 Sep;48(9):5232-5243. doi: 10.1002/mp.15066. Epub 2021 Jul 27.
The purpose of this work was to assess a proof of concept for a novel method for predicting proton stopping power ratios (SPRs) based on a pair of dual-energy CT generated virtual monoenergetic (VM) images.
A rapid kV-switching dual-energy CT scanner was used to acquire Gemstone Spectral Imaging (GSI) and 120 kV conventional single-energy CT (SECT) image data of the CIRS 062M phantom. The proposed method was applied to every possible pairing of VM images between 40 and 140 keV to find the optimal energy pairs for SPR prediction in lung tissue, soft tissue, and bone. The predicted SPRs were compared against SPRs predicted from the SECT data using the conventional SECT-based method. The impact of different scan and reconstruction parameters was also investigated.
The SPR residual root mean square errors (RMSE) yielded by the optimal pairs were 7.2% for lung tissue, 0.4% for soft tissue, and 0.8% for bone. While no direct comparison could be made to other DECT-based methods for SPR prediction, as these methods could not be directly implemented on a fast kV-switching system, the SPR RMSEs for soft tissue and bone in Table 4 are comparable to RMSEs reported in the literature. For the conventional SECT-based method, the SPR RMSEs were 5.9% for lung tissue, 0.9% for soft tissue, and 5.1% for bone.
The proposed method is a valid alternative to, and has the potential to improve upon, the conventional SECT-based method for predicting SPRs. The formalism used in the method is applied directly, with no approximations made on our part, and requires neither prior knowledge of the spectra nor calibration with a phantom. This work presents a way of optimizing the proposed method for a specific scanner by determining the optimal energy pairs to use as input and demonstrates the method's robustness to different levels of ASiR-V, reconstruction kernels, and dose levels.
本研究旨在评估一种基于双能 CT 生成的虚拟单能（VM）图像对质子阻止比（SPR）进行预测的新概念方法的可行性。
使用快速千伏切换双能 CT 扫描仪获取 CIRS 062M 体模的宝石能谱成像（GSI）和 120kV 常规单能 CT（SECT）图像数据。将所提出的方法应用于 40keV 至 140keV 之间的每一对 VM 图像，以找到预测肺组织、软组织和骨 SPR 的最佳能量对。将预测的 SPR 与使用常规 SECT 方法从 SECT 数据预测的 SPR 进行比较。还研究了不同扫描和重建参数的影响。
由最佳对生成的 SPR 残差均方根误差（RMSE）分别为肺组织 7.2%、软组织 0.4%和骨 0.8%。虽然无法与其他基于 DECT 的 SPR 预测方法进行直接比较，因为这些方法无法直接在快速千伏切换系统上实现，但表 4 中软组织和骨的 SPR RMSE 与文献中报道的 RMSE 相当。对于常规 SECT 方法，肺组织的 SPR RMSE 为 5.9%，软组织为 0.9%，骨为 5.1%。
该方法是一种可行的替代传统 SECT 方法的方法，有潜力提高 SPR 预测的准确性。所提出的方法的公式化直接应用，没有进行任何近似，也不需要对光谱进行先验知识或使用体模进行校准。本研究通过确定作为输入使用的最佳能量对，为特定扫描仪优化了所提出的方法，并证明了该方法对不同的 ASiR-V、重建核和剂量水平的稳健性。
