ESTRO 2020 Abstract Book

S719 ESTRO 2020

brain tissue and to further understand the distribution of TTFields. PO-1356 Ion Stopping Powers and Dual Energy CT Numbers of Animal Tissues for Monte Carlo Dose Calculations L. Zeng 1 , F.M. Michael 1 1 Shanghai Proton and Heavy Ion Center, Department of Medical Physics, Shanghai, China Purpose or Objective For ion beam treatment, most treatment planning systems(TPSs) use a pencil beam algorithm that calculates dose distributions using depth dose data measured in water and an algorithm that converts the X-ray computed tomography number of a given material to its linear stopping power relative to water(RLSP). Recently some TPSs have started using Monte Carlo type dose calculations. These calculations typically need the physical density and elemental composition of the tissues to determine penetration and calculate dose. Material and Methods Samples of 10 different animal tissues were obtained and packed in regularity-shaped containers including brain. heart. fat. cartilage. hard bone. muscle. liver. kidney and lung. The physical density of each sample was measured. The samples were then scanned with a dual energy CT scanner. Additionally, The samples were placed between the exit of a beaming and a Peak Finder to measure single- spot integrated depth dose distributions. Beams of protons accelerated to energies of 118.0,167.3,and222.1Mev and carbon ions accelerated to 216.7, 322.8 and 430 MeV/u were used.

Figure 2:Measured single spot integrate depth dose distribution for 167.3 MeV proton for each sample. empty box. air and water placed between the radiation head and the Peak Finder. Conclusion Data for converting dual energy CT number for Monte Carlo dose calculation were obtained as well as data for verifying the correct conversion. A software workflow for the use of Siemens DEXCT images for Monte Carlo in the SPHIC in the future was suggested PO-1357 Creating individually computed head models to simulate TTFields distribution Z. Bomzon 1 , A. Kinzel 2 , N. Urman 1 , S. Levi 1 , A. Naveh 1 , D. Manzur 1 , H.S. Hershkovich 1 1 Novocure Ltd., Research and Development, Haifa, Israel ; 2 Novocure GmbH, Medical, Munich, Germany Purpose or Objective Tumor Treating Fields (TTFields) are locally applied alternating electric fields of intermediate frequency used to treat glioblastoma multiforme (200 kHz). Currently, TTFields are also investigated in clinical phase III trials in other solid cancers. TTFields distribution in the tissue is affected by the position of the transducer arrays delivering the therapy, but also by the patient’s anatomy and the electric properties of tissue and tumor. For investigating the influence of TTFields distribution on patient outcome, we aimed to design realistic, patient-specific computational head models in a rapid manner that is robust even when MRI image quality is restricted. Material and Methods We created our patient models using a detailed head model of a healthy person as a deformable template. After pre-processing (denoising and reducing background noise, if needed: super-resolution algorithms), we manually segmented and masked the tumor to leave only healthy tissue in the MRI. This is then registered to the template space transforming the patient space to template space. In the next step, the template is deformed into the patient space by inverse transformation before placing back the tumor to create the full patient model. Landmarks on the patient’s head are automatically identified for positioning of the transducer arrays to include them into the model. We then simulated field distribution with the Finite Elements Method (Sim4Life V3.0, ZMT-Zurich). Results We simulated distribution in 340 TTFields-treated patients of the EF-14 trial that led to the therapy approval in GBM. Our method enables to accurately contour tissues known to have a great impact on the electric field distribution such as the skull, scalp, CSF, or ventricles, a fundamental prerequisite for the subsequent study investigating correlation of TTFields spatial distribution and patient outcome. Conclusion Our method for fast patient-specific model creation enabled us to rapidly create patient-specific head models even when the quality of the available MRI images was low. This ultimately prepared the ground for investigations of

Figure1: PTW Peak Finder and measurement setup Results For each tissue a dual-energy CT index was derived. This index may be used for determining the elemental composition classification for each tissue used in the Monte Carlo calculations. RLSPs were derived for each tissue for verification of the correct conversion function. In addition. two pairs of data. the Bragg peak width and the distal gradient. relative electron density and effective atomic number for each tissue as the specific material indicators were obtained respectively.