ESTRO 2025 - Abstract Book

S3576

Physics - Optimisation, algorithms and applications for ion beam treatment planning

ESTRO 2025

Conclusion: We have proposed and validated an innovative, eco-friendly experimental setup that leverages the capabilities of 3D-printing. This method enables the creation of patient-specific shapes that can be produced quickly, owing to their hollow design. Additionally, by utilising reusable materials such as plastic or copper spheres, our approach reduces unnecessary waste. This development lowers costs, enhances conformity, and increases PS treatments and experiments' efficiency and environmental impact.

Keywords: passive scattering, FLASH, eco-friendly

References: [1] J. Perl, J. Shin, J. Schuemann, B. Faddegon, and H. Paganetti, “TOPAS: An innovative proton Monte Carlo platform for research and clinical applications,” Medical Physics, vol. 39, no. 11, 2012, issn: 00942405. doi: 10.1118/1.4758060. [2] B. Faddegon, J. Ramos-Mèndez, J. Schuemann, et al., “The TOPAS tool for particle simulation, a Monte Carlo simulation tool for physics, biology and clinical research,” Physica Medica, vol. 72, 2020, issn: 1724191X. doi: 10.1016/j.ejmp.2020.03.019. [3] J. Gostick, Z. Khan, T. Tranter, et al., “PoreSpy: A Python Toolkit for Quantitative Analysis of Porous Media Images,” Journal of Open Source Software, vol. 4, no. 37, 2019. doi: 10.21105/joss.01296.

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Digital Poster A novel method to determine tissue mass density and chemical composition with a photon counting CT reducing uncertainties in proton range estimation Nina Tilly, David Tilly Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden Purpose/Objective: The aim of this study was to reduce the uncertainty in proton range estimation by using a Photon Counting CT (PCCT). To the best of our knowledge this is the first time where the full spectral information is utilised to go from HU to mass density (MD) and chemical composition for different tissues. Material/Methods: Measurements of tissue-equivalent inserts in a CIRS phantom were used to calibrate both the PCCT and a single energy CT (SECT) at our hospital, and for evaluating the tissue characterisation model. A novel method was developed using the spectral information from the PCCT to enhance the tissue composition estimates. Each material was described by a linear combination of a few base materials, determined by a least square fit vs the measured HU. The PCCT model was tested using a leave-one-out approach where the insert to be evaluated was not included in the calibration. To facilitate comparison with prior work, stopping power ratios (SPR) were calculated. For reference, we used the HU to SPR tables in clinical use for proton therapy for our SECT. Having the estimated mass density and chemical composition of each tissue makes it possible to run Monte Carlo calculations [1] to see what these errors in SPR really mean in proton range. Results: Results show that our PCCT base material model reduced the SPR uncertainties compared to SECT. The errors in lung inhale tissue SPR went down from 19.0% (SECT) to 12.6% (PCCT). For soft tissues the highest errors were 2.1% (SECT) and 1.4% (PCCT) and for bone-equivalent tissues 5.6% (SECT) and 3.6% (PCCT). Monte Carlo range calculations were performed for 100 and 200 MeV protons using the theoretical material composition and our model composition. For 200 MeV protons also simulations in water with a 3 cm slab of these material compositions positioned at 5 to 8 cm depth were performed. All range differences for our model material