Investigating particle track topology for range telescopes in particle radiography using convolutional neural networks. (2nd November 2021)

• Record Type: Journal Article
• Authors: Pettersen, Helge Egil Seime; Aehle, Max; Alme, Johan; Barnaföldi, Gergely Gábor; Borshchov, Vyacheslav; van den Brink, Anthony; Chaar, Mamdouh; Eikeland, Viljar; Feofilov, Grigory; Garth, Christoph; Gauger, Nicolas R.; Genov, Georgi; Grøttvik, Ola; Helstrup, Håvard; Igolkin, Sergey; Keidel, Ralf; Kobdaj, Chinorat; Kortus, Tobias; Leonhardt, Viktor; Mehendale, Shruti; Mulawade, Raju Ningappa; Odland, Odd Harald; Papp, Gábor; Peitzmann, Thomas; Piersimoni, Pierluigi; Protsenko, Maksym; Rehman, Attiq Ur; Richter, Matthias; Santana, Joshua; Schilling, Alexander; Seco, Joao; Songmoolnak, Arnon; Sølie, Jarle Rambo; Tambave, Ganesh; Tymchuk, Ihor; Ullaland, Kjetil; Varga-Kofarago, Monika; Volz, Lennart; Wagner, Boris; Wendzel, Steffen; Wiebel, Alexander; Xiao, RenZheng; Yang, Shiming; Yokoyama, Hiroki; Zillien, Sebastian; Röhrich, Dieter;
• Is Part Of: Acta oncologica. Volume 60:Number 11(2021)
• Page Start: 1413
• Page End: 1418
• Publication Date: 2021-11-02
• Subjects: Proton computed tomography -- machine learning -- Monte Carlo simulation -- track reconstruction -- convolutional neural network -- secondary particles; Oncology -- Periodicals; Cancer -- Treatment -- Periodicals; 616.992
• DOI: 10.1080/0284186X.2021.1949037
• Languages: English
• ISSNs: 0284-186X

Abstract

Abstract: 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.