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Detail

Peter Kuess
Priv.-Doz. Mag. Peter Kuess, PhD

Department of Radiation Oncology
Position: Research Associate (Postdoc)

ORCID: 0000-0003-2961-1692
T +43 1 40400 72730
peter.kuess@meduniwien.ac.at

Further Information

Keywords

Artificial Intelligence; Dose-Response Relationship, Radiation; Heavy Ion Radiotherapy; Image Processing, Computer-Assisted; Radiation Oncology; Radiation Protection; Radiotherapy

Research group(s)

Research interests

  • Proton and ion beam therapy
  • Adaptive radiotherapy 
  • Image-guided particle therapy
  • AI in radiotherapy

Techniques, methods & infrastructure

  • Dosimetry in proton and carbon ion beams
  • Medical image processing
  • Neural networks for radiotherapy
  • multi-parametric MRI

Selected publications

  1. Kuess, P. et al., 2017. Lateral response heterogeneity of Bragg peak ionization chambers for narrow-beam photon and proton dosimetry. Physics in Medicine & Biology, 62(24), pp.9189–9206. Available at: http://dx.doi.org/10.1088/1361-6560/aa955e.
  2. Khachonkham, S. et al., 2018. Characteristic of EBT-XD and EBT3 radiochromic film dosimetry for photon and proton beams. Physics in Medicine & Biology, 63(6), p.065007. Available at: http://dx.doi.org/10.1088/1361-6560/aab1ee.
  3. Kuess, P. et al., 2017. Association between pathology and texture features of multi parametric MRI of the prostate. Physics in Medicine & Biology, 62(19), pp.7833–7854. Available at: http://dx.doi.org/10.1088/1361-6560/aa884d.
  4. Zimmermann, L. et al. (2022) ‘An MRI sequence independent convolutional neural network for synthetic head CT generation in proton therapy’, Zeitschrift für Medizinische Physik, 32(2), pp. 218–227. Available at: http://dx.doi.org/10.1016/j.zemedi.2021.10.003.
  5. Heilemann, G. et al. (2023) ‘Generating deliverable DICOM RT treatment plans for prostate VMAT by predicting MLC motion sequences with an encoder‐decoder network’, Medical Physics, 50(8), pp. 5088–5094. Available at: https://doi.org/10.1002/mp.16545.