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Physik-Institut Group of Jan Unkelbach

Treatment plan optimization

Treatment planning for radiotherapy is based on two main components: Dose calculation algorithms and mathematical optimization algorithms. Dose calculation algorithms use physical models to describe the interaction of radiation in tissue to calculate the distribution of absorbed radiation dose in the patient. Mathematical optimization methods are used to optimize intensities and incident directions of external radiation fields to irradiate the tumor while minimizing the radiation dose to surrounding normal tissues. Our group has worked on many problems related to the further development of optimization algorithms for treatment planning.

Our main projects in the field of treatment plan optimization are:

  1. In the context of our project on combined proton-photon radiotherapy, we work on treatment planning algorithms to simultaneously optimize IMRT and IMPT dose distributions to determine the optimal combination of both modalities.
  2. In the context of our project on spatiotemporal fractionation, we work on treatment planning algorithms based on biologically effective dose (BED) instead of physical dose. This allows the simultaneous optimization of multiple distinct dose distributions for different fractions.
  3. In the context of both projects, we work on the development of novel robust optimization techniques, which incorporate uncertainty in the IMRT/IMPT optimization problem.
  4. We work on treatment planning methods for patients with high metastatic tumor burden in whom it is not possible to deliver a curative radiation dose to all lesions without exceeding normal tissue constraints. Here, we work on cell-kill based objective functions to optimally distribute the radiation dose over all lesions.
  5. Building on algorithms for multileaf collimator leaf sequencing and direct aperture optimization, we work on methods and tools to create deliverable treatments plans in in-house research software. We further work on tools to import these treatment plans into the commercial treatment planning system Eclipse to allow clinical translation of our research.

Publications:

  1. S. Fabiano, N. Torelli, D. Papp, and J. Unkelbach, “A novel stochastic optimization method for handling misalignments of proton and photon doses in combined treatments,” Phys Med Biol, vol. 67, no. 18, Sep. 2022, doi: 10.1088/1361-6560/ac858f.
  2. S. Fabiano, M. Bangert, M. Guckenberger, and J. Unkelbach, “Accounting for Range Uncertainties in the Optimization of Combined Proton-Photon Treatments Via Stochastic Optimization,” Int. J. Radiat. Oncol. Biol. Phys., vol. 108, no. 3, pp. 792–801, Nov. 2020, doi: 10.1016/j.ijrobp.2020.04.029.
  3. L. Marc, S. Fabiano, N. Wahl, C. Linsenmeier, A. J. Lomax, and J. Unkelbach, “Combined proton-photon treatment for breast cancer,” Phys Med Biol, vol. 66, no. 23, Nov. 2021, doi: 10.1088/1361-6560/ac36a3.
  4. J. Unkelbach, M. Bangert, K. De Amorim Bernstein, N. Andratschke, and M. Guckenberger, “Optimization of combined proton-photon treatments,” Radiother Oncol, vol. 128, no. 1, pp. 133–138, Jul. 2018, doi: 10.1016/j.radonc.2017.12.031.
  5. J. Unkelbach, D. Papp, M. R. Gaddy, N. Andratschke, T. Hong, and M. Guckenberger, “Spatiotemporal fractionation schemes for liver stereotactic body radiotherapy,” Radiother Oncol, vol. 125, no. 2, pp. 357–364, Nov. 2017, doi: 10.1016/j.radonc.2017.09.003.
  6. J. Unkelbach et al., “Robust radiotherapy planning,” Physics in Medicine & Biology, vol. 63, no. 22, p. 22TR02, 2018.

Prior work we build on:

  1. A. Cassioli and J. Unkelbach, “Aperture shape optimization for IMRT treatment planning,” Physics in medicine and biology, vol. 58, no. 2, pp. 301–18, Jan. 2013, doi: 10.1088/0031-9155/58/2/301.