Author: Julia Bauer, Tianxue Du, Katia Parodi, Marco Pinto, Thomas Tessonnier 👨🔬
Affiliation: Department of Medical Physics, Ludwig-Maximilians-Universität (LMU) München, Heidelberg Ion Beam Therapy Center (HIT), Department of Radiation Oncology, Heidelberg University Hospital, Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich) 🌍
Purpose:
Carbon ion therapy could benefit from range verification due to its sensitivity to range uncertainties. Positron emission tomography (PET) aids in this and comparing irradiation-induced PET signals to predicted positron emitter distributions (PED) is one viable way. We developed an analytical approach for 3D PED prediction in carbon ion therapy, providing a faster alternative to Monte Carlo (MC) simulations with potential for integration into analytical treatment planning systems (TPS).
Methods:
A computational framework for analytical 3D PED prediction, based on the filtering approach [1-3] and pencil beam algorithm (PBA) [4], was proposed. The modified filtering approach combines laterally integrated depth PED derived from the dose distribution and parameterizations, while the PBA extends the framework into 3D PED using lateral PED properties, beam, and material information. This solution was validated using clinical cases, comparing the analytical predictions against MC simulations and PET measurements.
Results:
We analyzed two patients with brain and neck tumors undergoing PET measurements following scanned carbon ion irradiation [5]. The analytical and MC activity distributions demonstrated a good match in range with mean deviation less than 1 mm. Overall a lower range difference was observed between the measured PET signals and the analytical activity patterns.
Conclusion:
The obtained results demonstrate the capability of our analytical approach to predict PET images for range verification in carbon ion therapy, offering faster predictions than MC simulations while maintaining good accuracy. The solution proposed also offers the possibility of a straightforward integration into TPS by leveraging the commonly used PBAs present in analytical carbon ion dose engines.
[1] Parodi et al., Phys. Med. Biol. 51, 1991(2006).
[2] Hofmann et al., Phys. Med. Biol. 64, 105022(2019).
[3] Vasic et al., Appl. Radiat. Isot. 213, 111479(2024).
[4] Soukup et al., Phys. Med. Biol. 50, 5089(2005).
[5] Bauer et al., Radiother Oncol. 107(2), 218(2013).