Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark. Author to whom any correspondence should be addressed.
Phys Med Biol. 2017 Dec 14;63(1):015012. doi: 10.1088/1361-6560/aa952f.
Dual energy CT (DECT) has been shown, in theoretical and phantom studies, to improve the stopping power ratio (SPR) determination used for proton treatment planning compared to the use of single energy CT (SECT). However, it has not been shown that this also extends to organic tissues. The purpose of this study was therefore to investigate the accuracy of SPR estimation for fresh pork and beef tissue samples used as surrogates of human tissues. The reference SPRs for fourteen tissue samples, which included fat, muscle and femur bone, were measured using proton pencil beams. The tissue samples were subsequently CT scanned using four different scanners with different dual energy acquisition modes, giving in total six DECT-based SPR estimations for each sample. The SPR was estimated using a proprietary algorithm (syngo.via DE Rho/Z Maps, Siemens Healthcare, Forchheim, Germany) for extracting the electron density and the effective atomic number. SECT images were also acquired and SECT-based SPR estimations were performed using a clinical Hounsfield look-up table. The mean and standard deviation of the SPR over large volume-of-interests were calculated. For the six different DECT acquisition methods, the root-mean-square errors (RMSEs) for the SPR estimates over all tissue samples were between 0.9% and 1.5%. For the SECT-based SPR estimation the RMSE was 2.8%. For one DECT acquisition method, a positive bias was seen in the SPR estimates, having a mean error of 1.3%. The largest errors were found in the very dense cortical bone from a beef femur. This study confirms the advantages of DECT-based SPR estimation although good results were also obtained using SECT for most tissues.
双能 CT(DECT)在理论和体模研究中已被证明,与使用单能 CT(SECT)相比,可提高质子治疗计划中使用的阻止本领比(SPR)的确定。然而,目前还没有证明这也适用于有机组织。因此,本研究旨在调查使用新鲜猪肉和牛肉组织样本作为人体组织替代物时 SPR 估计的准确性。使用质子铅笔束测量了包括脂肪、肌肉和股骨在内的 14 个组织样本的参考 SPR。随后,使用具有不同双能采集模式的四种不同扫描仪对组织样本进行 CT 扫描,总共为每个样本进行了六种基于 DECT 的 SPR 估计。使用专有算法(syngo.via DE Rho/Z Maps,Siemens Healthcare,Forchheim,Germany)来提取电子密度和有效原子数,估算 SPR。还采集了 SECT 图像,并使用临床 Hounsfield 查找表进行了基于 SECT 的 SPR 估算。计算了大体积感兴趣区域内 SPR 的平均值和标准差。对于六种不同的 DECT 采集方法,所有组织样本的 SPR 估计的均方根误差(RMSE)在 0.9%到 1.5%之间。对于基于 SECT 的 SPR 估计,RMSE 为 2.8%。对于一种 DECT 采集方法,SPR 估计值存在正偏差,平均误差为 1.3%。最大的误差出现在来自牛股骨的非常致密的皮质骨中。这项研究证实了基于 DECT 的 SPR 估计的优势,尽管对于大多数组织,使用 SECT 也可以获得良好的结果。