Result: Reconstruction of proton relative stopping power with a granular calorimeter detector model.
Title:
Reconstruction of proton relative stopping power with a granular calorimeter detector model.
Authors:
Aehle, M.1 (AUTHOR), Alme, J.2 (AUTHOR), Barnaföldi, G. G.3 (AUTHOR), Bíró, G.3 (AUTHOR) biro.gabor@wigner.hun-ren.hu, Bodova, T.2 (AUTHOR), Borshchov, V.4 (AUTHOR), Brink, A. van den2 (AUTHOR), Chaar, M.2 (AUTHOR), Dudás, B.5 (AUTHOR), Eikeland, V.6 (AUTHOR), Feofilov, G.7 (AUTHOR), Garth, C.8 (AUTHOR), Gauger, N. R.1 (AUTHOR), Grøttvik, O.2 (AUTHOR), Helstrup, H.9 (AUTHOR), Igolkin, S.7 (AUTHOR), Jólesz, Z.3,5 (AUTHOR), Keidel, R.10 (AUTHOR), Kobdaj, C.11 (AUTHOR), Kortus, T.1 (AUTHOR)
Source:
International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics. 7/30/2025, Vol. 40 Issue 21, p1-15. 15p.
Subject Terms:
Database:
Academic Search Index
Further Information
Proton computed tomography (pCT) aims to facilitate precise dose planning for hadron therapy, a promising and effective method for cancer treatment. Hadron therapy utilizes protons and heavy ions to deliver well-focused doses of radiation, leveraging the Bragg peak phenomenon to target tumors while sparing healthy tissues. The Bergen pCT Collaboration aims to develop a novel pCT scanner, and accompanying reconstruction algorithms to overcome current limitations. This paper focuses on advancing the track and image reconstruction algorithms, thereby enhancing the precision of the dose planning and reducing side effects of hadron therapy. A neural network aided track reconstruction method is presented. [ABSTRACT FROM AUTHOR]