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
Keywords
Artificial Intelligence; Dose-Response Relationship, Radiation; Heavy Ion Radiotherapy; Image Processing, Computer-Assisted; Radiation Oncology; Radiation Protection; Radiotherapy
Research group(s)
- Medical Radiation Research
Head: Dietmar Georg
Research Area: The vision of our group is the optimization of the treatment outcome of radiation oncology, alone or in combination with established chemotherapy or novel targeted strategies of drug treatment, with conventional photon or innovative ion-beams.
Members:
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
- 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.
- 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.
- 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.
- 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.
- 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.