English

Proton radiographs using position-sensitive silicon detectors and high-resolution scintillators

Medical Physics 2022-05-04 v1 Applied Physics Instrumentation and Detectors

Abstract

Proton therapy is a cancer treatment technique currently in growth worldwide. It offers advantages with respect to conventional X-ray and γ\gamma-ray radiotherapy, in particular, a better control of the dose deposition allowing to reach a higher conformity in the treatments. Therefore, it causes less damage to the surrounding healthy tissue and less secondary effects. However, in order to take full advantage of its potential, improvements in treatment planning and dose verification are required. A new prototype of proton Computed Tomography scanner is proposed to design more accurate and precise treatment plans for proton therapy. Here, results obtained from an experiment performed using a 100-MeV proton beam at the CCB facility in Krakow (Poland) are presented. Proton radiographs of PMMA samples of 50-mm thickness with spatial patterns in aluminum were taken. Their properties were studied, including reproduction of the dimensions, spatial resolution and sensitivity to different materials. They demonstrate the capabilities of the system to produce images with protons. Structures of up to 2 mm are nicely resolved and the sensitivity of the system was enough to distinguish thicknesses of 10 mm of aluminum or PMMA. This constitutes a first step to validate the device as a proton radiography scanner previous to the future tests as a proton CT scanner.

Keywords

Cite

@article{arxiv.2111.08681,
  title  = {Proton radiographs using position-sensitive silicon detectors and high-resolution scintillators},
  author = {J. A. Briz and A. N. Nerio and C. Ballesteros and M. J. G. Borge and P. Martínez and A. Perea and V. G. Távora and O. Tengblad and M. Ciemala and A. Maj and P. Olko and W. Parol and A. Pedracka and B. Sowicki and M. Zieblinski and E. Nácher},
  journal= {arXiv preprint arXiv:2111.08681},
  year   = {2022}
}

Comments

7 pages, 11 figures, submitted to IEEE TNS ANIMMA 2021 Conference Proceedings

R2 v1 2026-06-24T07:41:07.138Z