Article ; Online: Investigating particle track topology for range telescopes in particle radiography using convolutional neural networks.
Acta oncologica (Stockholm, Sweden)
2021 Volume 60, Issue 11, Page(s) 1413–1418
Abstract: Background: Proton computed tomography (pCT) and radiography (pRad) are proposed modalities for improved treatment plan accuracy and : Material and methods: The CNN was trained by simulation and reconstruction of tens of millions of proton and helium ...
Abstract | Background: Proton computed tomography (pCT) and radiography (pRad) are proposed modalities for improved treatment plan accuracy and Material and methods: The CNN was trained by simulation and reconstruction of tens of millions of proton and helium tracks. The CNN filter was then compared to simple energy loss threshold methods using the Area Under the Receiver Operating Characteristics curve (AUROC), and by comparing the image quality and Water Equivalent Path Length (WEPL) error of proton and helium radiographs filtered with the same methods. Results: The CNN method led to a considerable improvement of the AUROC, from 74.3% to 97.5% with protons and from 94.2% to 99.5% with helium. The CNN filtering reduced the WEPL error in the helium radiograph from 1.03 mm to 0.93 mm while no improvement was seen in the CNN filtered pRads. Conclusion: The CNN improved the filtering of proton and helium tracks. Only in the helium radiograph did this lead to improved image quality. |
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MeSH term(s) | Humans ; Image Processing, Computer-Assisted ; Monte Carlo Method ; Neural Networks, Computer ; Phantoms, Imaging ; Radiography ; Telescopes |
Language | English |
Publishing date | 2021-07-14 |
Publishing country | England |
Document type | Journal Article |
ZDB-ID | 896449-x |
ISSN | 1651-226X ; 0349-652X ; 0284-186X ; 1100-1704 |
ISSN (online) | 1651-226X |
ISSN | 0349-652X ; 0284-186X ; 1100-1704 |
DOI | 10.1080/0284186X.2021.1949037 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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