Dr. Yafei Xing Profile

Dr. Yafei Xing

at Univ Catholique de Louvain

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Proceedings Article | 17 November 2017 Paper

Improvement of Bragg peak shift estimation using dimensionality reduction techniques and predictive linear modeling

Yafei Xing, Benoit Macq

Proceedings Volume 10572, 1057215 (2017) https://doi.org/10.1117/12.2285608

KEYWORDS: Principal component analysis, Monte Carlo methods, Data modeling, Optical simulations, Picosecond phenomena, Cameras, Tumors, Computed tomography, Error analysis, Device simulation

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With the emergence of clinical prototypes and first patient acquisitions for proton therapy, the research on prompt gamma imaging is aiming at making most use of the prompt gamma data for in vivo estimation of any shift from expected Bragg peak (BP). The simple problem of matching the measured prompt gamma profile of each pencil beam with a reference simulation from the treatment plan is actually made complex by uncertainties which can translate into distortions during treatment. We will illustrate this challenge and demonstrate the robustness of a predictive linear model we proposed for BP shift estimation based on principal component analysis (PCA) method. It considered the first clinical knife-edge slit camera design in use with anthropomorphic phantom CT data. Particularly, 4115 error scenarios were simulated for the learning model. PCA was applied to the training input randomly chosen from 500 scenarios for eliminating data collinearities. A total variance of 99.95% was used for representing the testing input from 3615 scenarios. This model improved the BP shift estimation by an average of 63±19% in a range between -2.5% and 86%, comparing to our previous profile shift (PS) method. The robustness of our method was demonstrated by a comparative study conducted by applying 1000 times Poisson noise to each profile. 67% cases obtained by the learning model had lower prediction errors than those obtained by PS method. The estimation accuracy ranged between 0.31 ± 0.22 mm and 1.84 ± 8.98 mm for the learning model, while for PS method it ranged between 0.3 ± 0.25 mm and 20.71 ± 8.38 mm.

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