Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75287, USA.
Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.
Med Phys. 2018 Apr;45(4):1491-1503. doi: 10.1002/mp.12796. Epub 2018 Feb 23.
Accurate calculation of proton stopping power ratio (SPR) relative to water is crucial to proton therapy treatment planning, since SPR affects prediction of beam range. Current standard practice derives SPR using a single CT scan. Recent studies showed that dual-energy CT (DECT) offers advantages to accurately determine SPR. One method to further improve accuracy is to incorporate prior knowledge on human tissue composition through a dictionary approach. In addition, it is also suggested that using CT images with multiple (more than two) energy channels, i.e., multi-energy CT (MECT), can further improve accuracy. In this paper, we proposed a sparse dictionary-based method to convert CT numbers of DECT or MECT to elemental composition (EC) and relative electron density (rED) for SPR computation.
A dictionary was constructed to include materials generated based on human tissues of known compositions. For a voxel with CT numbers of different energy channels, its EC and rED are determined subject to a constraint that the resulting EC is a linear non-negative combination of only a few tissues in the dictionary. We formulated this as a non-convex optimization problem. A novel algorithm was designed to solve the problem. The proposed method has a unified structure to handle both DECT and MECT with different number of channels. We tested our method in both simulation and experimental studies.
Average errors of SPR in experimental studies were 0.70% in DECT, 0.53% in MECT with three energy channels, and 0.45% in MECT with four channels. We also studied the impact of parameter values and established appropriate parameter values for our method.
The proposed method can accurately calculate SPR using DECT and MECT. The results suggest that using more energy channels may improve the SPR estimation accuracy.
质子阻止本领比（SPR）相对于水的精确计算对于质子治疗计划至关重要，因为 SPR 会影响束流射程的预测。目前的标准做法是使用单次 CT 扫描来获得 SPR。最近的研究表明，双能 CT（DECT）在准确确定 SPR 方面具有优势。进一步提高准确性的一种方法是通过字典方法纳入人体组织成分的先验知识。此外，还建议使用具有多个（两个以上）能量通道的 CT 图像，即多能量 CT（MECT），以进一步提高准确性。在本文中，我们提出了一种基于稀疏字典的方法，将 DECT 或 MECT 的 CT 数转换为元素组成（EC）和相对电子密度（rED），以计算 SPR。
构建了一个字典，其中包括基于已知成分的人体组织生成的材料。对于具有不同能量通道 CT 数的体素，其 EC 和 rED 是根据约束条件确定的，即得到的 EC 是字典中仅少数几种组织的线性非负组合。我们将其表示为一个非凸优化问题。设计了一种新的算法来解决这个问题。所提出的方法具有统一的结构，可以处理具有不同通道数的 DECT 和 MECT。我们在模拟和实验研究中都测试了我们的方法。
实验研究中 SPR 的平均误差在 DECT 中为 0.70%，在具有三个能量通道的 MECT 中为 0.53%，在具有四个通道的 MECT 中为 0.45%。我们还研究了参数值的影响，并为我们的方法确定了合适的参数值。
所提出的方法可以使用 DECT 和 MECT 准确计算 SPR。结果表明，使用更多的能量通道可以提高 SPR 估计的准确性。
