ESTRO 2020 Abstract book

S352 ESTRO 2020

provided either by the TPS vendor or simulated in-house using FLUKA2011 and GATE/Geant4, respectively. Material and Methods The fluence differential in energy was scored dividing the sum of track lengths by the volume (Papiez and Battista, 1994), which was subsequently used to calculate the energy spectrum at 500 depths in water in 1 mm steps for 58 initial carbon ion energies (ranging from 120.0 to 402.8 MeV/u in 5 MeV/u steps). While a dedicated beam model was applied including the full description of the Nozzle using GATE-RTionV1.0 (Geant4.10.03), the FLUKA2011 simulations mimicked the influence of the Nozzle by offsetting and superimposing mono-energetic beams such that measured depth dose distributions were resembled. Treatment plans (TPs) were generated for five patient cases and four box shaped targets in water at varying depths and side lengths using the TPS clinical beam model and subsequently the RBE weighted dose was re- computed using the fluence tables based on Geant4. Results The fluence spectra of the primary and secondary particles simulated with Geant4 and FLUKA agreed generally well, but exhibiting two major systematic differences (see Fig.1): the lithium and beryllium yield over the entire energy range and the hydrogen and helium fluence below about 1 MeV was considerably lower in Geant4 compared to FLUKA. However, these differences were not expected to have an impact on RBE, as the former had the lowest fluence of all secondary fragments and the latter, hyperthermal ions, was reported to have a negligible clinical relevance (Elsässer et al. , 2009). Using the two energy spectra (FLUKA vs. GEANT4) to calculate the RBE weighted dose distributions resulted in average in deviations of less than 1% for each patient or water cases in the entrance up to the end of the target region, with a maximum local deviation of 3% at the distal edge of the target. In the fragmentation tail higher discrepancies up to 5% in average were found for deep seated targets.

Conclusion Using energy spectra derived from two different Monte Carlo codes and methods to account for the nozzle contribution resulted in clinically acceptable agreement with respect to RBE weighted dose. The results confirmed, as an independent validation of the clinical beam model, that the open source and publicly available Geant4 code can also be used to generate basic beam data required by the LEM I model. OC-0578 Ultrasound-guided PBS proton beam tracking in lung using a statistical motion model M. Krieger 1,2 , A. Giger 3,4 , A. Duetschler 1,2 , C. Jud 3,4 , P.C. Cattin 3,4 , R.V. Salomir 5,6 , O. Bieri 3,7 , D.C. Weber 1,8,9 , A.J. Lomax 1,2 , Y. Zhang 1 1 Paul Scherrer Institute, Centre for Proton Therapy, Villigen PSI, Switzerland ; 2 ETH Zurich, Department of Physics, Zurich, Switzerland ; 3 University of Basel, Department of Biomedical Engineering, Allschwil, Switzerland ; 4 University of Basel, Center for medical Image Analysis & Navigation, Allschwil, Switzerland ; 5 University of Geneva, Image Guieded Interventions Laboratory, Geneva, Switzerland ; 6 University Hospitals of Geneva, Radiology Division, Geneva, Switzerland ; 7 University Hospital Basel, Department of Radiology, Basel, Switzerland ; 8 Inselspital Bern, Department of Radiation Oncology, Bern, Switzerland ; 9 University Hospital Zurich, Department of Radiation Oncology, Zurich, Switzerland Purpose or Objective Pencil beam scanned proton therapy (PBS) naturally facilitates tumour tracking as long as the deformable 3D motion of the whole patient geometry is known in real- time – an impossible task with current online IGRT approaches. In this study, the feasibility of PBS tracking based on the reconstruction of tumour and lung motion using a statistical motion model and liver ultrasound (US) as a real-time motion surrogate has been investigated. Material and Methods Simultaneous free-breathing 4DMR and liver US images were acquired for five volunteers, resulting in 690-1056 variable 4DMR volumes per volunteer, with a temporal resolution of 0.4-0.6s and acquired over 7-11min. Deformation vector fields (DVF), extracted from each 4DMRI, were used to generate 5 synthetic 4DCT datasets from the same static lung patient CT (Fig 1a). Each dataset contained 99-159 full breathing cycles (Fig 1b) with the corresponding DVFs considered to represent the ground- truth motion for each 4DCT dataset. Using the simultaneously acquired liver US, a patient-specific motion model was created, based on principal component analysis and Gaussian process regression using the first 631-965 motion states per volunteer. Based on the corresponding US signal, this model was then used to predict DVFs of the lung for the last 35s of motion of each dataset not included in the model building (predicted motion). A 2-field PBS plan was optimised on the CTV of the reference CT (Fig 1a) and deformable 3D beam tracking simulated by adapting pencil beam positions laterally and in depth based on either the ground-truth (ideal) or predicted motions (realistic tracking). Resulting 4D dose distributions were compared in terms of absolute dose difference volume histograms (DDVH, VOI=CTV+20mm), and dose volume histograms (DVH) of the CTV.

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