[OA051] Toward a new treatment planning system accounting for in-vivo proton range verification in proton therapy. (August 2018)

• Record Type: Journal Article
• Authors: Tian, Liheng; Dedes, Georgios; Landry, Guillaume; Kamp, Florian; Niepel, Katharina; Belka, Claus; Parodi, Katia
• Is Part Of: Physica medica. Volume 52(2018)Supplement 1
• Page Start: 21
• Publication Date: 2018-08
• Subjects: Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2018.06.123
• Languages: English
• ISSNs: 1120-1797

Abstract

Abstract : Purpose: Proton therapy is widely used in state-of-the-art radiotherapy. However, precision of proton therapy could be affected by proton range uncertainties due to e.g. anatomy change, patient setting uncertainties. Prompt gamma (PG) imaging is investigated to monitor proton range in vivo by detecting PG emitted by nuclear de-excitation processes in the beam path. Despite ideal correlation between the penetration depth of PG signal and proton range in most cases, few literature studies reported that this correlation could deteriorate because of tissue heterogeneities. Hence, in this study, we investigate a new treatment planning approach accounting for PG imaging. Methods: A research computational platform, combining Monte Carlo (MC, Geant4) pre-calculated pencil beams (subPB) with the Matlab-based TPS engine CERR (A computational Environment for Radiotherapy Research), was introduced for treatment planning. To optimize current treatment plans considering PG imaging, first, a MC treatment plan is created using a particle extension of CERR. Thereby, the PG emission and dose distribution for each subPB is obtained. Secondly, the PG fall-off position is evaluated and compared to the proton range (80% distal dose fall-off) for all subPBs. SubPBs with reliable PG-dose correlation are visualized in terms of beam-eye-view location and individual dose delivery. Few of these "good-correlation" subPBs are boosted in the new treatment plan via manual or automated selection to enable good detectability. Then, a re-optimized plan is created and recalculated on the same patient CT using Geant4 for evaluation. Results: The new treatment planning approach was applied to 3 head & neck tumor patients. The dose distribution of the original and re-optimized treatment plan was found to be of comparable dosimetric quality. The positions and PG-dose correlations of highest-intensity subPBs in the original plan were random, while in the optimized plan the highest-intensity subPBs are with good PG-dose correlation and uniformly distributed across the entire field for a reliable and thorough monitoring. Conclusions: A new treatment planning approach has been proposed, which could improve the treatment planning process in proton therapy by integrating the PG-based in vivo monitoring of the beam range for a safer and more controllable proton treatment. EU-MSCA GA n. 675265 (OMA).