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Investigating particle track topology for range telescopes in particle radiography using convolutional neural networks.

Helge Egil Seime PettersenMax AehleJohan AlmeGergely Gábor BarnaföldiVyacheslav BorshchovAnthony van den BrinkMamdouh ChaarViljar EikelandGrigory FeofilovChristoph GarthNicolas R GaugerGeorgi GenovOla GrøttvikHåvard HelstrupSergey IgolkinRalf KeidelChinorat KobdajTobias KortusViktor LeonhardtShruti MehendaleRaju Ningappa MulawadeOdd Harald OdlandGábor PappThomas PeitzmannPierluigi PiersimoniMaksym ProtsenkoAttiq Ur RehmanMatthias RichterJoshua SantanaAlexander SchillingJoao SecoArnon SongmoolnakJarle Rambo SølieGanesh TambaveIhor TymchukKjetil UllalandMonika Varga-KofaragoLennart VolzBoris WagnerSteffen WendzelAlexander WiebelRenZheng XiaoShiming YangHiroki YokoyamaSebastian ZillienDieter Röhrich
Published in: Acta oncologica (Stockholm, Sweden) (2021)
The CNN improved the filtering of proton and helium tracks. Only in the helium radiograph did this lead to improved image quality.
Keyphrases
  • convolutional neural network
  • image quality
  • deep learning
  • computed tomography
  • dual energy
  • magnetic resonance imaging