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Related Experiment Video

Updated: Nov 12, 2025

Neutron Radiography and Computed Tomography of Biological Systems at the Oak Ridge National Laboratory's High Flux Isotope Reactor
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Proton path reconstruction for proton computed tomography using neural networks.

T Ackernley1,2, G Casse1,2, M Cristoforetti2

  • 1Oliver Lodge, Department of Physics, University of Liverpool, Oxford Street, L69 7ZE Liverpool, United Kingdom.

Physics in Medicine and Biology
|March 18, 2021
PubMed
Summary
This summary is machine-generated.

A new deep neural network (DNN) method improves proton path reconstruction in proton computed tomography by accurately handling nuclear interactions, outperforming the traditional most likely path (MLP) formalism.

Keywords:
deep learningmlppct

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Area of Science:

  • Medical Physics
  • Computational Imaging
  • Particle Physics

Background:

  • Proton computed tomography (pCT) utilizes proton path reconstruction for imaging.
  • The most likely path (MLP) formalism is the standard for precise proton path reconstruction.
  • MLP struggles with nuclear interactions, often discarding relevant data.

Purpose of the Study:

  • To develop a novel deep neural network (DNN) method for proton path reconstruction.
  • To improve accuracy in pCT by effectively incorporating nuclear interactions.
  • To assess the computational efficiency of the DNN method compared to MLP.

Main Methods:

  • Proton path reconstruction using a deep neural network (DNN) algorithm.
  • Comparison of DNN performance against the most likely path (MLP) formalism.
  • Analysis of proton tracks considering both multiple coulomb scattering (MCS) and nuclear interactions.

Main Results:

  • The DNN method achieved MLP-equivalent accuracy when only MCS was present.
  • The DNN method demonstrated increased accuracy when nuclear interactions were included.
  • The DNN algorithm showed potential for significantly faster computation than MLP.

Conclusions:

  • Deep neural networks offer a promising alternative for proton path reconstruction in pCT.
  • The DNN approach enhances accuracy by better managing nuclear interactions.
  • The DNN method presents a computationally efficient advancement over traditional MLP techniques.