Our study aims to quantify the impact of spectral separation on achieved theoretical prediction accuracy of proton-stopping power when the volume discrepancy between calibration phantom and scanned object is observed. Such discrepancy can be commonly seen in our CSI pediatric patients. One of the representative image-domain DECT models is employed on a virtual phantom to derive electron density and effective atomic number for a total of 34 ICRU standard human tissues. The spectral pairs used in this study are 90 kVp/140 kVp, without and with 0.1 mm to 0.5 mm additional tin filter. The two DECT images are reconstructed via a conventional filtered back projection algorithm (FBP) on simulated noiseless projection data. The best-predicted accuracy occurs at a spectral pair of 90 kVp/140 kVp with a 0.3 mm tin filter, and the root-mean-squared average error is 0.12% for tissue substitutes. The results reveal that the selected image-domain model is sensitive to spectral pair deviation when there is a discrepancy between calibration and scanning conditions. This study suggests that an optimization process may be needed for clinically available DECT scanners to yield the best proton-stopping power estimation.
本研究旨在量化当校准模体与扫描物体间存在体积差异时，光谱分离对质子阻止本领理论预测精度的影响。此类差异常见于儿童中枢神经系统肿瘤患者。研究采用代表性图像域双能CT模型，在虚拟模体中对34种ICRU标准人体组织进行电子密度与有效原子数的推导。使用的光谱组合为90 kVp/140 kVp，分别设置无附加锡滤过与0.1-0.5 mm锡滤过条件。基于无噪声模拟投影数据，采用传统滤波反投影算法重建双能CT图像。结果显示：在90 kVp/140 kVp配合0.3 mm锡滤过的光谱组合下，组织替代物的质子阻止本领预测精度最优，均方根平均误差为0.12%。研究表明当校准条件与扫描条件存在差异时，所选图像域模型对光谱组合偏差具有敏感性。这提示临床可用的双能CT扫描仪可能需要通过优化流程才能获得最佳的质子阻止本领估算结果。
肿瘤(癌症)患者之家