SU-G-TeP1-06

• Verfasst von: Botas Sanmartín, Pablo [VerfasserIn]
• Verfasst von: Grassberger, Clemens [VerfasserIn]
• Verfasst von: Sharp, G. [VerfasserIn]
• Verfasst von: Qin, N. [VerfasserIn]
• Verfasst von: Jia, X. [VerfasserIn]
• Verfasst von: Jiang, S. [VerfasserIn]
• Verfasst von: Paganetti, Harald [VerfasserIn]
• Titel: SU-G-TeP1-06
• Titelzusatz: fast GPU framework for four-dimensional Monte Carlo in adaptive intensity modulated proton therapy (IMPT) for mobile tumors
• Verf.angabe: P. Botas, C. Grassberger, G. Sharp, N. Qin, X. Jia, S. Jiang, H. Paganetti
• E-Jahr: 2016
• Jahr: 07 June 2016
• Umfang: 1 S.
• Fussnoten: Gesehen am 18.05.2020
• Titel Quelle: Enthalten in: Medical physics
• Ort Quelle: Hoboken, NJ : Wiley, 1974
• Jahr Quelle: 2016
• Band/Heft Quelle: 43(2016), 6Part26, Seite 3653
• ISSN Quelle: 2473-4209
• ISSN Quelle: 1522-8541
• DOI: doi:10.1118/1.4956996
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Cancer
• Sach-SW: Computed tomography
• Sach-SW: Databases
• Sach-SW: Image registration
• Sach-SW: Lungs
• Sach-SW: Medical treatment planning
• Sach-SW: Monte Carlo methods
• Sach-SW: Parallel processing
• Sach-SW: Proton therapy
• Sach-SW: Protons
• K10plus-PPN: 1698447965

Abstract

Purpose: To demonstrate the feasibility of fast Monte Carlo (MC) treatment planning and verification using four-dimensional CT (4DCT) for adaptive IMPT for lung cancer patients. Methods: A validated GPU MC code, gPMC, has been linked to the patient database at our institution and employed to compute the dose-influence matrices (Dij) on the planning CT (pCT). The pCT is an average of the respiratory motion of the patient. The Dijs and patient structures were fed to the optimizer to calculate a treatment plan. To validate the plan against motion, a 4D dose distribution averaged over the possible starting phases is calculated using the 4DCT and a model of the time structure of the delivered spot map. The dose is accumulated using vector maps created by a GPU-accelerated deformable image registration program (DIR) from each phase of the 4DCT to the reference phase using the B-spline method. Calculation of the Dij matrices and the DIR are performed on a cluster, with each field and vector map calculated in parallel. Results: The Dij production takes ∼3.5s per beamlet for 10e6 protons, depending on the energy and the CT size. Generating a plan with 4D simulation of 1000 spots in 4 fields takes approximately 1h. To test the framework, IMPT plans for 10 lung cancer patients were generated for validation. Differences between the planned and the delivered dose of 19% in dose to some organs at risk and 1.4/21.1% in target mean dose/homogeneity with respect to the plan were observed, suggesting potential for improvement if adaptation is considered. Conclusion: A fast MC treatment planning framework has been developed that allows reliable plan design and verification for mobile targets and adaptation of treatment plans. This will significantly impact treatments for lung tumors, as 4D-MC dose calculations can now become part of planning strategies.