Ackernley, T, Casse, G 
ORCID: 0000-0002-8516-237X and Cristoforetti, M
(2021)
Proton path reconstruction for proton computed tomography using neural networks.
PHYSICS IN MEDICINE AND BIOLOGY, 66 (7).
075015-075015.
Abstract
The most likely path formalism (MLP) is widely established as the most statistically precise method for proton path reconstruction in proton computed tomography. However, while this method accounts for small-angle multiple coulomb scattering (MCS) and energy loss, inelastic nuclear interactions play an influential role in a significant number of proton paths. By applying cuts based on energy and direction, tracks influenced by nuclear interactions are largely discarded from the MLP analysis. In this work we propose a new method to estimate the proton paths based on a deep neural network (DNN). Through this approach, estimates of proton paths equivalent to MLP predictions have been achieved in the case where only MCS occurs, together with an increased accuracy when nuclear interactions are present. Moreover, our tests indicate that the DNN algorithm can be considerably faster than the MLP algorithm.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | pct, deep learning, mlp |
| Divisions: | Faculty of Science and Engineering > School of Physical Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 03 Sep 2021 15:31 |
| Last Modified: | 18 Jan 2023 21:30 |
| DOI: | 10.1088/1361-6560/abf00f |
| Open Access URL: | https://arxiv.org/pdf/2010.00427.pdf |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3135772 |
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