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A Filtering Approach for PET and PG Predictions in a Proton Treatment Planning System

M Pinto1*, K Kroeniger1, J Bauer2,3, R Nilsson4, E Traneus4, K Parodi1, (1) Ludwig-Maximilians-Universitat Munchen, Department for Medical Physics, Garching b. Munchen, DE, (2) Heidelberg University Hospital, Department of Radiation Oncology, Heidelberg, DE, (3) National Centre for Radiation Oncology NCRO, Heidelberg Institute for Radiation Oncology HIRO, Heidelberg, DE, (4) RaySearch Laboratories AB, Research, Stockholm, SE

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: To present a fast analytical method for calculating positron-emitter and prompt-gamma distributions in the research version of a commercial treatment planning system (TPS) for proton pencil beam scanning.

Methods: A filtering formalism was used to predict radiation-induced positron-emitter production and extended to estimation of energy-resolved prompt-gamma distributions, using filters based on Monte Carlo simulations and applied to arbitrary tissues. The implementation for the positron emission tomography (PET) monitoring case accounts for different acquisition time windows, washout effects and the use of a Gaussian kernel to model the PET scanner response. For the case of prompt-gamma monitoring, a method was devised to consider arbitrary energy selection windows, a feature essential to use the filtering approach for any of the spatial and spectroscopic prompt-gamma monitoring techniques. Both methods were validated against Monte Carlo simulations for four patients treated with scanned proton beams. Positron-emitter predictions were additionally validated against respective PET monitoring data.

Results: Longitudinal shifts between depth profiles from analytical and Monte Carlo calculations were within -1.7 and 0.9 mm, with maximum standard deviation of 0.9 mm and 1.1 mm, for positron-emitters and prompt-gamma shifts, respectively. These values are within the typical range found in the literature when comparing TPS and Monte Carlo dose calculations. Normalized mean absolute errors were within 1.2 and 5.3%. When comparing measured and predicted PET data, the more complex case yielded an average shift of 3 mm, while all other cases were below absolute average shifts of 1.1 mm. Normalized mean absolute errors were below 7.2% for all cases.

Conclusion: A novel solution to predict positron-emitter and prompt-gamma distributions in a treatment planning system is proposed, enabling calculation times of only a few seconds to minutes for entire patient cases, which is suitable for integration in daily clinical routine.

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Funding Support, Disclosures, and Conflict of Interest: LMU and RaySearch Laboratories have a research collaboration and license agreement. Erik Traneus and Rasmus Nilsson are employees of RaySearch Laboratories AB. Marco Pinto has a granted patent on apparatus for particle therapy verification using prompt gammas.

Keywords

Not Applicable / None Entered.

Taxonomy

TH- External Beam- Particle/high LET therapy: Range verification (in vivo/phantom): prompt gamma/PET

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