Department of Electrical and Systems Engineering, Washington University, St. Louis, MO, 63130, USA.
Medical Physics Graduate Program, Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, 23298, USA.
Med Phys. 2018 May;45(5):2129-2142. doi: 10.1002/mp.12875. Epub 2018 Apr 1.
The purpose of this study was to assess the performance of a novel dual-energy CT (DECT) approach for proton stopping power ratio (SPR) mapping that integrates image reconstruction and material characterization using a joint statistical image reconstruction (JSIR) method based on a linear basis vector model (BVM). A systematic comparison between the JSIR-BVM method and previously described DECT image- and sinogram-domain decomposition approaches is also carried out on synthetic data.
The JSIR-BVM method was implemented to estimate the electron densities and mean excitation energies (I-values) required by the Bethe equation for SPR mapping. In addition, image- and sinogram-domain DECT methods based on three available SPR models including BVM were implemented for comparison. The intrinsic SPR modeling accuracy of the three models was first validated. Synthetic DECT transmission sinograms of two 330 mm diameter phantoms each containing 17 soft and bony tissues (for a total of 34) of known composition were then generated with spectra of 90 and 140 kVp. The estimation accuracy of the reconstructed SPR images were evaluated for the seven investigated methods. The impact of phantom size and insert location on SPR estimation accuracy was also investigated.
All three selected DECT-SPR models predict the SPR of all tissue types with less than 0.2% RMS errors under idealized conditions with no reconstruction uncertainties. When applied to synthetic sinograms, the JSIR-BVM method achieves the best performance with mean and RMS-average errors of less than 0.05% and 0.3%, respectively, for all noise levels, while the image- and sinogram-domain decomposition methods show increasing mean and RMS-average errors with increasing noise level. The JSIR-BVM method also reduces statistical SPR variation by sixfold compared to other methods. A 25% phantom diameter change causes up to 4% SPR differences for the image-domain decomposition approach, while the JSIR-BVM method and sinogram-domain decomposition methods are insensitive to size change.
Among all the investigated methods, the JSIR-BVM method achieves the best performance for SPR estimation in our simulation phantom study. This novel method is robust with respect to sinogram noise and residual beam-hardening effects, yielding SPR estimation errors comparable to intrinsic BVM modeling error. In contrast, the achievable SPR estimation accuracy of the image- and sinogram-domain decomposition methods is dominated by the CT image intensity uncertainties introduced by the reconstruction and decomposition processes.
本研究旨在评估一种新型的基于联合统计图像重建（JSIR）方法的双能 CT（DECT）质子阻止本领比（SPR）映射方法的性能，该方法结合了图像重建和材料特征化，使用基于线性基向量模型（BVM）的方法。还在合成数据上对 JSIR-BVM 方法与之前描述的 DECT 图像域和谱域分解方法进行了系统比较。
实施了 JSIR-BVM 方法，以估计用于 SPR 映射的贝塞方程所需的电子密度和平均激发能（I 值）。此外，还实施了基于三种可用 SPR 模型（包括 BVM）的图像域和谱域 DECT 方法进行比较。首先验证了三种模型的内在 SPR 建模准确性。然后，使用 90kVp 和 140kVp 的谱生成了两个直径为 330mm 的包含 17 种软组织和骨组织（总共 34 种）的已知成分的模拟 DECT 透射谱。评估了七种研究方法对重建的 SPR 图像的估计准确性。还研究了体模大小和插入位置对 SPR 估计准确性的影响。
在没有重建不确定性的理想化条件下，所有三种选定的 DECT-SPR 模型对所有组织类型的 SPR 预测的 RMS 误差均小于 0.2%。当应用于合成谱时，JSIR-BVM 方法在所有噪声水平下的平均和 RMS 平均误差均小于 0.05%和 0.3%，而图像域和谱域分解方法的平均和 RMS 平均误差随着噪声水平的增加而增加。JSIR-BVM 方法还将统计 SPR 变化减少了六倍，与其他方法相比。体模直径变化 25%会导致图像域分解方法的 SPR 差异达到 4%，而 JSIR-BVM 方法和谱域分解方法对尺寸变化不敏感。
在所研究的所有方法中，JSIR-BVM 方法在我们的模拟体模研究中实现了最佳的 SPR 估计性能。该新方法对谱噪声和残余束硬化效应具有鲁棒性，产生的 SPR 估计误差可与内在 BVM 建模误差相媲美。相比之下，图像域和谱域分解方法的可实现 SPR 估计精度受重建和分解过程中 CT 图像强度不确定性的影响。
