Regression analysis for paths inference in a novel Proton CT system

Yu, Miao, Gong, Liyun, Ye, Xujiong and Allinson, Nigel (2017) Regression analysis for paths inference in a novel Proton CT system. In: ISP 2017, 4 - 5 Dec 2017, London.

ISP2017_paper.pdf - Whole Document

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


In this work, we analyse the proton paths inference for the construction of CT imagery based on a new proton CT proton system, which can record multiple proton paths/residual energies. Based on the recorded paths of multiple protons, every proton path is inferred. The inferred proton paths can then be used for the residual energies detection and CT imagery construction for analyzing a specific tissue. Different regression methods (linear regression and Gaussian process regression models) are exploited for the path inference of every proton in this work. The studies on a
recorded proton trajectories dataset show that the Gaussian process regression method achieves better accuracies for the path inference, from both path assignment accuracy and root mean square errors (RMSEs) studies.

Keywords:proton, regression
Subjects:G Mathematical and Computer Sciences > G310 Applied Statistics
G Mathematical and Computer Sciences > G340 Statistical Modelling
G Mathematical and Computer Sciences > G140 Numerical Analysis
Divisions:College of Science > School of Computer Science
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ID Code:31018
Deposited On:12 Mar 2018 09:58

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