Investigating particle track topology for range telescopes in particle radiography using convolutional neural networks
| Autor: | Shruti Vineet Mehendale, Sebastian Zillien, A. Songmoolnak, Matthias Richter, Kjetil Ullaland, Håvard Helstrup, Boris Wagner, Dieter Røhrich, Joao Seco, RenZheng Xiao, Steffen Wendzel, M. Protsenko, S.N. Igolkin, Jarle Rambo Sølie, Max Aehle, Monika Varga-Kofarago, I. Tymchuk, Chinorat Kobdaj, Tobias Kortus, Georgi Genov, Johan Alme, Shiming Yang, Christoph Garth, Viktor Leonhardt, Helge Egil Seime Pettersen, Pierluigi Piersimoni, V.N. Borshchov, Alexander Schilling, Ralf Keidel, Thomas Peitzmann, Gergely Gabor Barnafoldi, O.H. Odland, Raju Ningappa Mulawade, Ola Slettevoll Grøttvik, Viljar Nilsen Eikeland, Attiq Ur Rehman, Hiroki Yokoyama, G. A. Feofilov, Ganesh Jagannath Tambave, Anthony van den Brink, Lennart Volz, Mamdouh Chaar, Gábor Papp, Joshua Santana, Nicolas R. Gauger, Alexander Wiebel |
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| Přispěvatelé: | Sub Subatomic Physics (SAP), Subatomic Physics |
| Rok vydání: | 2021 |
| Předmět: |
congenital
hereditary and neonatal diseases and abnormalities Radiography education Monte Carlo method convolutional neural network Topology (electrical circuits) Convolutional neural network secondary particles parasitic diseases Image Processing Computer-Assisted Medicine Humans Radiology Nuclear Medicine and imaging Computer vision Proton therapy Monte Carlo simulation Range (particle radiation) business.industry Phantoms Imaging Track (disk drive) track reconstruction General Medicine Hematology Proton computed tomography bacterial infections and mycoses machine learning Oncology Radiology Nuclear Medicine and imaging Particle Artificial intelligence Neural Networks Computer business Monte Carlo Method hormones hormone substitutes and hormone antagonists Telescopes |
| Zdroj: | Acta Oncologica, 60(11), 1413. Taylor and Francis Ltd. |
| ISSN: | 0284-186X |
| DOI: | 10.6084/m9.figshare.14980391 |
| Popis: | Background: Proton computed tomography (pCT) and radiography (pRad) are proposed modalities for improved treatment plan accuracy and in situ treatment validation in proton therapy. The pCT system of the Bergen pCT collaboration is able to handle very high particle intensities by means of track reconstruction. However, incorrectly reconstructed and secondary tracks degrade the image quality. We have investigated whether a convolutional neural network (CNN)-based filter is able to improve the image quality. 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. |
| Databáze: | OpenAIRE |
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