Radiotherapy Optimization

Group leader: Dr. Mark Bangert



Rosario's AAPM presentation about the role anatomical variations for xerostomia prediction has been selected as Science Highlight 

Niklas' and Hans-Peter's papers on analytical probabilistic modeling and on its application for C12 have been published

Hubert's work on using machine learning methods in combination with dosiomics features for xerostomia brediction has been published in Frontiers in Oncology.

matRad, our open-source software for dose calculation and optimiztiation, keeps growing.

Our work revolves around the incorporation of physical and mathematical models into the treatment optimization process for radiation therapy. Currently, we focus on three topics:

  • Machine learning and numerical optimization techniques for the quantification and minimization of uncertainties in radiation therapy
  • Data mining to infer predictive models for the outcome of radiation therapy
  • Incorporation of additional optimization parameters into automated radiation therapy planning

All projects are carried out in collaboration with reknown colleagues in the field:

We are constantly looking for motivated individuals coming from a physics, math, and/or computer science background with a genuine interest in medical physics in radiation oncology. If you are interested to join our research group for your Bachelor, Master, and PhD thesis or for a postdoc position, do not hesitate and get in touch.



  • Amit Antony (PhD student)
  • Rosario Astaburuaga (Student assistant)
  • Dr. Mark Bangert (Group leader)  → CV → Google citations
  • Hubert Gabrys (PhD student)
  • Ahmad Neishabouri (Student assistant)
  • Niklas Wahl (PhD student)
  • Hans-Peter Wieser (PhD student)


With our work we try to support open science. As such we make a lot of our programming work freely available via our group's github site. Most notably, we maintain the Matlab-based open-source toolkit for dose calculation and optimization in radiotherapy optimization matRad.


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