TY  - JOUR
AU  - Pettersen, Helge Egil Seime
AU  - Aehle, Max
AU  - Alme, Johan
AU  - Barnaföldi, Gergely Gábor
AU  - Borshchov, Vyacheslav
AU  - van den Brink, Anthony
AU  - Chaar, Mamdouh
AU  - Eikeland, Viljar
AU  - Feofilov, Grigory
AU  - Garth, Christoph
AU  - Gauger, Nicolas R
AU  - Genov, Georgi
AU  - Grøttvik, Ola
AU  - Helstrup, Håvard
AU  - Igolkin, Sergey
AU  - Keidel, Ralf
AU  - Kobdaj, Chinorat
AU  - Kortus, Tobias
AU  - Leonhardt, Viktor
AU  - Mehendale, Shruti
AU  - Mulawade, Raju Ningappa
AU  - Odland, Odd Harald
AU  - Papp, Gábor
AU  - Peitzmann, Thomas
AU  - Piersimoni, Pierluigi
AU  - Protsenko, Maksym
AU  - Rehman, Attiq Ur
AU  - Richter, Matthias
AU  - Santana, Joshua
AU  - Schilling, Alexander
AU  - Seco, Joao
AU  - Songmoolnak, Arnon
AU  - Sølie, Jarle Rambo
AU  - Tambave, Ganesh
AU  - Tymchuk, Ihor
AU  - Ullaland, Kjetil
AU  - Varga-Kofarago, Monika
AU  - Volz, Lennart
AU  - Wagner, Boris
AU  - Wendzel, Steffen
AU  - Wiebel, Alexander
AU  - Xiao, RenZheng
AU  - Yang, Shiming
AU  - Yokoyama, Hiroki
AU  - Zillien, Sebastian
AU  - Röhrich, Dieter
TI  - Investigating particle track topology for range telescopes in particle radiography using convolutional neural networks.
JO  - Acta oncologica
VL  - 60
IS  - 11
SN  - 0001-6926
CY  - Abingdon
PB  - Taylor & Francis Group
M1  - DKFZ-2021-01577
SP  - 1413-1418
PY  - 2021
N1  - 2021 Nov;60(11):1413-1418
AB  - 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.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.The CNN method led to a considerable improvement of the AUROC, from 74.3
KW  - Monte Carlo simulation (Other)
KW  - Proton computed tomography (Other)
KW  - convolutional neural network (Other)
KW  - machine learning (Other)
KW  - secondary particles (Other)
KW  - track reconstruction (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:34259117
DO  - DOI:10.1080/0284186X.2021.1949037
UR  - https://inrepo02.dkfz.de/record/169833
ER  -