Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing 100191, China; School of Physics, Beihang University, Beijing 102206, China.
Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan.
Phys Med. 2021 Sep;89:182-192. doi: 10.1016/j.ejmp.2021.07.038. Epub 2021 Aug 12.
This study aims to estimate the proton stopping power ratio (SPR) by using 80-120 kV and 120 kV-6 MV dual-energy CT (DECT) in a fully simulation-based approach for proton therapy dose calculations.
Based on a virtual CT system, a two-step approach is applied to obtain the reference attenuation coefficient for image reconstruction. The effective atomic number (EAN) and electron density ratio (EDR) are estimated from two CT scans. The SPR is estimated using a calibration based on known materials to obtain a piecewise linear relationship between the EAN and the logarithm of the mean excitation energy, lnI. The calibration phantoms are constructed from reference tissue materials taken from ICRU Report 44. Our approach is evaluated through using the ICRP110 human phantom. The respective influences of noise and beam hardening effects are studied.
With the beam hardening correction applied, the results of 120 kV-6 MV DECT are comparable to those of 80-120 kV DECT in predicting the EAN, but the former demonstrated a clear improvement in predicting the EDR and SPR. The 120 kV-6 MV DECT is able to predict the SPR within an accuracy of 10% for lung tissue and 5% for pelvis tissue, thereby outperforming the 80-120 kV DECT.
The 120 kV-6 MV DECT is less sensitive to noise but more susceptible to beam hardening effects. By applying beam hardening correction, the 120 kV-6 MV DECT can predict the SPR more accurately than the 80-120 kV DECT. To utilize our DECT approach most effectively, high-quality reconstructed images are required.
本研究旨在通过完全基于模拟的方法,使用 80-120kV 和 120kV-6MV 双能 CT(DECT)来估计质子阻止比(SPR),用于质子治疗剂量计算。
基于虚拟 CT 系统,采用两步法获得图像重建的参考衰减系数。从两次 CT 扫描中估计有效原子数(EAN)和电子密度比(EDR)。通过基于已知材料的校准来估计 SPR,以获得 EAN 与平均激发能对数 lnI 之间的分段线性关系。校准体模由取自 ICRU 报告 44 的参考组织材料构建。我们的方法通过使用 ICRP110 人体体模进行评估。研究了噪声和束硬化效应的各自影响。
应用束硬化校正后,120kV-6MV DECT 的结果在预测 EAN 方面与 80-120kV DECT 相当,但前者在预测 EDR 和 SPR 方面明显改善。120kV-6MV DECT 能够以肺组织 10%和骨盆组织 5%的精度预测 SPR,优于 80-120kV DECT。
120kV-6MV DECT 对噪声的敏感性较低,但对束硬化效应的敏感性较高。通过应用束硬化校正,120kV-6MV DECT 可以比 80-120kV DECT 更准确地预测 SPR。为了最有效地利用我们的 DECT 方法,需要高质量的重建图像。