Niklas Wahl

Purpose: Carbon ions are radiobiologically more effective than photons and are beneficial for treating radioresistant gross tumour volumes (GTV). However, due to a reduced fractionation effect, they may be disadvantageous for treating infiltrative tumours, where healthy tissue inside the clinical target volume (CTV) must be protected through fractionation. This work addresses the question: what is the ideal combined photon-carbon ion fluence distribution for treating infiltrative tumours given a specific fraction allocation between photons and carbon ions? Methods: We present a method to simultaneously optimize sequentially delivered intensity modulated photon (IMRT) and carbon ion (CIRT) treatments based on cumulative biological effect, incorporating both the variable RBE of carbon ions and the fractionation effect within the linear quadratic model. The method is demonstrated for six Glioblastoma patients in comparison to current clinical standard of independently optimized CIRT - IMRT plans. Results: Compared to the reference plan, joint optimization strategies yield inhomogeneous photon and carbon ion dose distributions that cumulatively deliver a homogeneous biological effect distribution. In the optimal distributions, the dose to CTV is mostly delivered by photons while carbon ions are restricted to the GTV with variations depending on tumour size and location. Improvements in conformity of high dose regions are reflected by a mean EQD2 reduction of 3.29 ± 1.22 Gy in a dose fall-off margin around the CTV. Carbon ions may deliver higher doses to the center of the GTV, while photon contributions are increased at interfaces with CTV and critical structures. This results in a mean EQD2 reduction of 8.3 ± 2.28 Gy, where the brainstem abuts the target volumes. Conclusions: We have developed a biophysical model to optimize combined photon-carbon ion treatments. For six glioblastoma patient cases, we show that our approach results in a more targeted application of carbon ions that (1) reduces dose in normal tissues within the target volume which can only be protected through fractionation (2) boosts central target volume regions in order to reduce integral dose. Joint optimization of IMRT - CIRT treatments enable the exploration of a new spectrum of plans that can better address physical and radiobiological treatment planning challenges.

Purpose: To investigate the feasibility and accuracy of proton dose calculations with artificial neural networks (ANNs) in challenging three-dimensional (3D) anatomies. Methods: A novel proton dose calculation approach was designed based on the application of a long short-term memory (LSTM) network. It processes the 3D geometry as a sequence of two-dimensional (2D) computed tomography slices and outputs a corresponding sequence of 2D slices that forms the 3D dose distribution. The general accuracy of the approach is investigated in comparison to Monte Carlo reference simulations and pencil beam dose calculations. We consider both artificial phantom geometries and clinically realistic lung cases for three different pencil beam energies. Results: For artificial phantom cases, the trained LSTM model achieved a 98.57% γ-index pass rate ([1%, 3 mm]) in comparison to MC simulations for a pencil beam with initial energy 104.25 MeV. For a lung patient case, we observe pass rates of 98.56%, 97.74%, and 94.51% for an initial energy of 67.85, 104.25, and 134.68 MeV, respectively. Applying the LSTM dose calculation on patient cases that were fully excluded from the training process yields an average γ-index pass rate of 97.85%. Conclusions: LSTM networks are well suited for proton dose calculation tasks. Further research, especially regarding model generalization and computational performance in comparison to established dose calculation methods, is warranted. © 2020 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine. [https://doi.org/10.1002/mp.14658]

Purpose We report on the development of the open-source cross-platform radiation treatment planning toolkit matRad and its comparison against validated treatment planning systems. The toolkit enables three-dimensional intensity-modulated radiation therapy treatment planning for photons, scanned protons and scanned carbon ions. Methods matRad is entirely written in Matlab and is freely available online. It re-implements well-established algorithms employing a modular and sequential software design to model the entire treatment planning workflow. It comprises core functionalities to import DICOM data, to calculate and optimize dose as well as a graphical user interface for visualization. matRad dose calculation algorithms (for carbon ions this also includes the computation of the relative biological effect) are compared against dose calculation results originating from clinically approved treatment planning systems. Results We observe three-dimensional γ-analysis pass rates ≥ 99.67% for all three radiation modalities utilizing a distance to agreement of 2 mm and a dose difference criterion of 2%. The computational efficiency of matRad is evaluated in a treatment planning study considering three different treatment scenarios for every radiation modality. For photons, we measure total run times of 145 s–1260 s for dose calculation and fluence optimization combined considering 4–72 beam orientations and 2608–13597 beamlets. For charged particles, we measure total run times of 63 s–993 s for dose calculation and fluence optimization combined considering 9963–45574 pencil beams. Using a CT and dose grid resolution of 0.3 cm3 requires a memory consumption of 1.59 GB–9.07 GB and 0.29 GB–17.94 GB for photons and charged particles, respectively. Conclusion The dosimetric accuracy, computational performance and open-source character of matRad encourages a future application of matRad for both educational and research purposes.