Radiotherapy Optimization
Dr. Niklas Wahl
Research Group Leader
Radiotherapy Treatment Planning relies on numerical dose calculation and optimization techniques. The available strategies and algorithms limit the potential treatment outcome. Our group researches new approaches going beyond the conventional dose paradigm, application of novel optimization methods, and integration of AI into the treatment planning workflow.
Introduction
Radiotherapy treatment planning is a heavily computerized process relying on accurate dose calculation and optimization to enable control of cancerous tissue with high dose while sparing the healthy tissue and critical organs.
The Radiotherapy Optimization Group researches the integration of physical, biological and mathematical models into the treatment planning process for radiation therapy. The focus lies on pre-clinical research and prototyping new approaches in dose calculation or dose optimization.
Currently, the key areas of research are:
Treatment planning “beyond dose”:
Conventional treatment planning prescribes dose (i.e., the energy absorbed by the tissue) to define treatment goals and toxicity endpoints. However, the biological response does also depend strongly on the radiation quality, particular for charged particle therapy, tissue types and biochemical environment. Thus, we are primarily interested in using other physical quantities with stronger response correlation (e.g., from nanodosimetry) for treatment planning and the use of artificial intelligence to directly plan on predicted outcome.
Combined treatments:
Locations like Heidelberg have access to many different irradiation modalities like photon, proton, and ion treatments, but in general, their individual availability varies hugely. Combining those modalities within the treatment regime can improve resource usage, but is far from trivial due to different biological effectiveness. We are interested in finding the optimal combination and planning strategies for such combined treatments
Integration of Artificial Intelligence:
The integration of AI into the treatment planning workflows shows to major potentials: They can either streamline otherwise tedious human tasks (e.g., organ segmentation or plan tuning) or accelerate otherwise computationally demanding numerical computations like Monte Carlo simulation ore optimization algorithms. Our primary focus here lies on AI-based dose calculation and using highly complex AI treatment outcome prediction models directly in plan optimization.
In all of our research we advocate for open science. Much of our research is executed within our open-source treatment planning toolkits matRad (www.matRad.org) and pyRadPlan.
Most of our research is carried out in collaboration with renowned colleagues in the field. Current collaborations include:
Dr. Gonzalo Cabal @ Clinica El Rosario Medellin
Prof. Bruce Faddegon @ University of California, San Francisco
Dr. Ali Ajdari @ Massachusetts General Hospital / Harvard Medical School
Dr. Malte Ellerbrock @ Heidelberg Ion-Beam Therapy Center
Prof. Philipp Hennig @ Max Planck Institute for Intelligent Systems/University Tübingen
Past collaborators:
Join our Research?
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 with Niklas Wahl.
People
Dr. Niklas Wahl (Group leader - CV)
- Dr. Amit Ben Antony Bennan (PostDoc)
- Tobias Becher (PhD Student)
- Remo Cristoforetti (PhD student)
- Simona Facchiano (PhD student)
- Jennifer Hardt (PhD student)
- Noa Homolka (PhD student)
- Tim Ortkamp (associated PhD student from KIT in the HIDSS4Health Program)
- Goran Stanic (PhD student), also associated with K. Giske's Group
- Lina Bucher (HiWi)
- Lisa Seckler (MSc Student)
- Florian Leininger (HiWi + MSc Student)
Code
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 dose calculation and optimization toolkit matRad for radiotherapy research and education.
Projects
Publications
Xiao, F. ; Radonic, D. ; Kriechbaum, M. ; Wahl, N. ; Neishabouri, A. ; Delopoulos, N. ; Parodi, K. ; Corradini, S. ; Belka, C. ; Kurz, C. ; Landry, G. ; Dedes, G.
Radonic, D. ; Xiao, F. ; Wahl, N. ; Voss, L. ; Neishabouri, A. ; Delopoulos, N. ; Marschner, S. ; Corradini, S. ; Belka, C. ; Dedes, G. ; Kurz, C. ; Landry, G.
Harrison, N. ; Kang, M. ; Liu, R. ; Charyyev, S. ; Wahl, N. ; Liu, W. ; Zhou, J. ; Higgins, K. A. ; Simone, C. B. ; Bradley, J. D. ; Dynan, W. S. ; Lin, L.
Han, Y. ; Geng, C. ; Altieri, S. ; Bortolussi, S. ; Liu, Y. ; Wahl, N. ; Tang, X.
Faddegon, B. ; Descovich, M. ; Chen, K. ; Ramos-Méndez, J. ; Iii, M. R. ; Wahl, N. ; Taylor, P. ; Griffin, K. ; Lee, C.
Stammer, P. ; Burigo, L. ; Jäkel, O. ; Frank, M. ; Wahl, N.
Liu, R. ; Charyyev, S. ; Wahl, N. ; Liu, W. ; Kang, M. ; Zhou, J. ; Yang, X. ; Baltazar, F. ; Palkowitsch, M. ; Higgins, K. ; Dynan, W. ; Bradley, J. ; Lin, L.
Foka, P. ; Mamaras, A. ; Skrjiel, D. ; Seco, J. ; Graeff, C. ; Pulia, M. ; Wieser, H.-P. ; Wahl, N.
Stammer, P. ; Burigo, L. ; Jäkel, O. ; Frank, M. ; Wahl, N.
Wieser, H.-P. ; Cisternas, E. ; Wahl, N. ; Ulrich, S. ; Stadler, A. ; Mescher, H. ; Müller, L.-R. ; Klinge, T. ; Gabrys, H. ; Burigo, L. N. ; Mairani, A. ; Ecker, S. ; Ackermann, B. ; Ellerbrock, M. ; Parodi, K. ; Jäkel, O. ; Bangert, M.
Unkelbach, J. ; Bortfeld, T. ; Craft, D. ; Alber, M. ; Bangert, M. ; Bokrantz, R. ; Chen, D. ; Li, R. ; Xing, L. ; Men, C. ; Nill, S. ; Papp, D. ; Romeijn, E. ; Salari, E.
The list includes publications from previous group leaders as well (Dr. Mark Bangert up until 2018).
For a full list of articles, theses etc. from the division, see here.
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