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  4. Strategies to improve the range verification of stochastic origin ensembles for low-count prompt gamma imaging
 
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Strategies to improve the range verification of stochastic origin ensembles for low-count prompt gamma imaging

Journal
Nuclear Engineering and Technology
Date Issued
2023-01-01
Author(s)
HSUAN-MING HUANG  
DOI
10.1016/j.net.2023.06.035
URI
https://scholars.lib.ntu.edu.tw/handle/123456789/635160
URL
https://api.elsevier.com/content/abstract/scopus_id/85165293010
Abstract
The stochastic origin ensembles method with resolution recovery (SOE-RR) has been proposed to reconstruct proton-induced prompt gammas (PGs), and the reconstructed PG image was used for range verification. However, due to low detection efficiency, the number of valid events is low. Such a low-count condition can degrade the accuracy of the SOE-RR method for proton range verification. In this study, we proposed two strategies to improve the reconstruction of the SOE-RR algorithm for low-count PG imaging. We also studied the number of iterations and repetitions required to achieve reliable range verification. We simulated a proton beam (108 protons) irradiated on a water phantom and used a two-layer Compton camera to detect 4.44-MeV PGs. Our simulated results show that combining the SOE-RR algorithm with restricted volume (SOE-RR-RV) can reduce the error of the estimation of the Bragg peak position from 5.0 mm to 2.5 mm. We also found that the SOE-RR-RV algorithm initialized using a back-projection image could improve the convergence rate while maintaining accurate range verification. Finally, we observed that the improved SOE-RR algorithm set for 60,000 iterations and 25 repetitions could provide reliable PG images. Based on the proposed reconstruction strategies, the SOE-RR algorithm has the potential to achieve a positioning error of 2.5 mm for low-count PG imaging.
Subjects
Compton camera | Origin ensemble | Prompt gamma imaging
SDGs

[SDGs]SDG3

Type
journal article

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