Extension of the Fermi-Eyges most-likely path in heterogeneous medium with prior knowledge information
Extension of the Fermi-Eyges most-likely path in heterogeneous medium with prior knowledge information
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DOI：
10.1088/1361-6560/aa955d
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发表时间：
2017-12-21
影响因子：
3.5
通讯作者：
Seco, Joao
中科院分区：
文献类型：
--
作者：
Collins-Fekete, Charles-Antoine;Bar, Esther;Seco, Joao
由于粒子在其整个路径中经历多次库仑散射偏转，粒子成像的空间分辨率较差。为了解释这些偏差，基于费米-艾格斯理论的贝叶斯改编，开发了最可能路径 (MLP) 形式。之前的工作计算了均匀水介质中的 MLP 形式作为初步估计。然而，这可能会降低 MLP 估计的准确性以及后续断层扫描重建可实现的分辨率。这项工作研究了引入有关介质成分和密度的先验知识以提高 MLP 准确性的潜在收益。为此，使用蒙特卡罗 (MC) Geant4 算法来模拟质子 (n = 10(6)) 穿过代表肺、腹部和头部的三个不同拟人模型。先验知识信息是从 (1) 地面实况的 MC 模拟 (MLP-GT) 或 (2) 最近的 DECT 材料分解技术 (MLP-DECT) 中收集的。使用先验知识方法重建的路径精度与（3）在均质水中重建的路径（MLP-Water）和（4）路径重建方法进行比较，其中质子路径通过随后的MLP-Water计算（MLP-Hull）投影到体模边界处的外壳上。对于每种路径重建方法，都会比较重建路径和 MC 路径之间的最大均方根误差 (RMSmax)。在每个体模中，MLP-Water 和考虑异质性的其他三种路径算法（肺部为 -33%，腹部为 -38%，头部为 -81%）之间的 RMSmax 有所降低，每种算法（MLP-DECT、MLP-GT 和 MLP-Hull）之间没有显着差异。总之，在 MLP 形式中引入先验知识会降低 MC 路径的 RMS 不确定性，但不会比使用更简单的赫尔轮廓算法更进一步。建议在未来的粒子成像应用中使用该赫尔算法。
Particle imaging suffers from poor spatial resolution due to the multiple Coulomb scattering deflections undergone by the particles throughout their path. To account for these deflections, a most-likely path (MLP) formalism was developed based on a Bayesian adaption of the Fermi-Eyges theory. Previous work calculated the MLP formalism in a homogeneous water medium as an initial estimate. However, this potentially reduces the accuracy of the MLP estimate as well as the achievable resolution of the subsequent tomographic reconstruction. This work investigates the potential gain of introducing prior-knowledge on the medium composition and density to improve the MLP accuracy. To do so, a Monte Carlo (MC) Geant4 algorithm was used to simulate protons (n = 10(6)) crossing three different anthropomorphic phantoms representing the lung, abdomen, and head. The prior-knowledge information is gathered from (1) the MC simulation for ground-truth (MLP-GT), or from (2) a recent DECT material decomposition technique (MLP-DECT). The reconstructed path accuracy using prior-knowledge methods is compared with (3) the path reconstructed in homogeneous water (MLP-Water) and (4) a path reconstruction method where the proton path is projected onto a Hull at the boundary of the phantom with a subsequent MLP-Water calculation (MLP-Hull). For each path reconstruction method, the maximal root-mean-square error (RMSmax) is compared between the reconstructed and the MC path. In every phantom, the RMSmax is decreased between the MLP-Water and the three other path algorithms that take into account heterogeneities (-33% for the lung, -38% for the abdomen and -81% for the head), with no significant differences between each (MLP-DECT, MLP-GT and MLP-Hull). In conclusion, the introduction of prior-knowledge in the MLP formalism decreases the RMS uncertainty to the MC path, but no further than the use of a simpler Hull contour algorithm. The use of this Hull algorithm is suggested for future particle imaging applications.