Toward a new treatment planning approach accounting for in vivo proton range verification. (30th October 2018)

• Record Type: Journal Article
• Authors: Tian, Liheng; Landry, Guillaume; Dedes, Georgios; Kamp, Florian; Pinto, Marco; Niepel, Katharina; Belka, Claus; Parodi, Katia
• Is Part Of: Physics in medicine & biology. Volume 63:Number 21(2018:Nov.)
• Publication Date: 2018-10-30
• Subjects: prompt gamma -- proton therapy -- range verification; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aae749
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Protons with modern pencil-beam scanning delivery are widely used in state-of-the-art radiotherapy. To reduce the unwanted effect of proton range uncertainties, prompt gamma (PG) monitoring is investigated and considered one of the most promising methods for real-time, in vivo range verification. Despite good correlation between the penetration depth of the PG signal and proton range in most cases, mismatch can occur especially because of tissue heterogeneities. Moreover, detectability and reproducibility of the prompt gamma signal critically depends on counting statistics. Nowadays, conventional treatment planning systems do not account for the degree of correlation between dose and PG signal nor the expected PG signal counting statistics, which considerably influences the possibility of a reliable verification of the intended beam range. Hence, in this project, we investigate a new treatment planning approach, in which the spot-by-spot conformities between PG and dose profiles (PG-dose correlation) as well as PG signal detectability and precision are taken into account based on a TPS optimizer. To investigate the feasibility of this idea, a research computational platform, combining Monte Carlo (MC, Geant4) pre-calculated pencil beams with the analytical Matlab-based TPS engine CERR, is used for treatment planning. Geant4 is employed for realistic simulation of the dose delivery and PG generation of all spots in the heterogeneous patient anatomy given by CT images. First of all, a treatment plan is created using a charged particle extension of CERR. Secondly, the PG fall-off positions of all individual pencil beams are evaluated and compared to the 80% distal dose fall-off positions. Thirdly, the PG-dose correlations of all spots are quantified. A new plan, in which a few spots with the best PG-dose correlation are boosted to ensure PG detectability with good precision, is then made. Finally, the optimized plan is fully recalculated on the same patient CT using Geant4, and the result is evaluated considering both plan quality and beam range monitorability. The evaluation shows that the re-optimized treatment plan is comparable to the initial plan in terms of dose distribution, dose averaged LET distribution and robustness, while fulfilling the set statistical conditions for reliable PG monitoring of the few automatically or manually selected spots. The method could thus complement, and for the selected pencil beams even overcome limitations of, alternative suggested approach such as pencil beam aggregation to provide sufficient counting statistics for precise PG range retrieval with good correlation to the treatment dose.