Clinical validation of a GPU-based Monte Carlo dose engine of a commercial treatment planning system for pencil beam scanning proton therapy. (August 2021)

• Record Type: Journal Article
• Authors: Fracchiolla, Francesco; Engwall, Erik; Janson, Martin; Tamm, Fredrik; Lorentini, Stefano; Fellin, Francesco; Bertolini, Mattia; Algranati, Carlo; Righetto, Roberto; Farace, Paolo; Amichetti, Maurizio; Schwarz, Marco
• Is Part Of: Physica medica. Volume 88(2021)
• Page Start: 226
• Page End: 234
• Publication Date: 2021-08
• Subjects: Proton therapy -- GPU monte carlo -- Clinical validation -- Dosimetric validation -- Calculation time; 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.2021.07.012
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: We presented the dosimetric validation of a clinical version of Monte Carlo running on GPU. A large pool of patients (4 4 0) was used for the validation. We demonstrated the great impact in terms of computation time the new dose engine will have on clinical workflows. Abstract: Purpose: To perform the validation of the GPU-based (Graphical Processing Unit based) proton Monte Carlo (MC) dose engine implemented in a commercial TPS (RayStation 10B) and to report final dose calculation times for clinical cases. Materials and Methods: 440 patients treated at the Proton Therapy Center of Trento, Italy, between 2018 and 2019 were selected for this study. 636 approved plans with 3361 beams computed with the clinically implemented CPU-MC dose engine (version 4.2 and 4.5), were used for the validation of the new algorithm. For each beam, the dose was recalculated using the new GPU-MC dose engine with the initial CPU computation settings and compared to the original CPU-MC dose. Beam dose difference distributions were studied to ensure that the two dose distributions were equal within the expected fluctuations of the MC statistical uncertainty ( s ) of each computation. Plan dose distributions were compared with respect to the dosimetric indices D98, D50 and D1 of all ROIs defined as targets. A complete assessment of the computation time as a function of s and dose grid voxel size was done. Results: The median over all mean beam dose differences between CPU- and GPU-MC was −0.01% and the median of the corresponding standard deviations was close to ( √ 2 s ) both for simulations with an s of 0.5% and 1.0% per beam. This shows that the two dose distributions can be considered equal. All the DVH indices showed an average difference below 0.04%. About half of the plans were computed with 1.0% statistical uncertainty on a 2 mm dose calculation grid, for which the median computation time was 5.2 s. The median computational speed for all plans in the study was 8.4 million protons/second. Conclusion: A validation of a clinical MC algorithm running on GPU was performed on a large pool of patients treated with pencil beam scanning proton therapy. We demonstrated that the differences with the previous CPU-based MC were only due to the intrinsic statistical fluctuations of the MC method, which translated to insignificant differences on plan dose level. The significant increase in dose calculation speed is expected to facilitate new clinical workflows.