P04.57 Creating patient-specific computational head models for the study of tissue-electric field interactions using deformable templates. (19th September 2018)

• Record Type: Journal Article
• Authors: Urman, N; Levy, S; Frenkel, A; Naveh, A; Hershkovich, H S; Kirson, E; Wenger, C; Lavy-Shahaf, G; Manzur, D; Yesharim, O; Bomzon, Z
• Is Part Of: Neuro-oncology. Volume 20(2018)Supplement 3
• Page Start: iii292
• Page End: iii292
• Publication Date: 2018-09-19
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noy139.291
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Background: Tumor Treating Fields (TTFields) are delivered to the brain through two pairs of transducer arrays placed on the patient's scalp. TTFields distribution in the brain depends the array's position, patient anatomy and the electric properties of the tissues and tumor. Preclinical studies show that the effect of TTFields is dose-dependent, and depends on the intensity of the TTFields. Therefore, it is important to understand how TTFields distribute in the brain, and how this distribution might influence disease progression. Studying TTFields distribution in the brain most often relies on numerically simulating delivery of TTFields to realistic computational models. Preparation of such models usually involves semi-automatic segmentation of MRI images, which can be a time consuming task. In addition, in clinical scenarios, MRI acquisition time is often reduced by increased slice spacing, limited field of view, or increased scan speed, leading inaccuracies in automated segmentation processes, and increasing the time required for model creation. Here we present a novel method designed to enable rapid model creation under those restrictions. Material and Methods: A highly detailed healthy head model serves as a deformable template from which patient models are created. The first step is pre-processing, involving denoising and background noise reduction, as well as super-resolution algorithms when needed. To create the patient model, the tumor is first segmented manually and masked, leaving only healthy tissues in the MRI, which is then registered to the template space to yield the transformation from patient space to template space. The template is then deformed into the patient space using the inverse transformation, and the tumor is placed back creating a full patient model. Next, automatic identification of landmarks on the patient's head is used to position the transducer arrays on the head, which are then introduced into the model. Finally, boundary conditions are set, and field distribution is simulated using Finite Differences Time Domain (FDTD) method (Sim4Life V3.0, ZMT-Zurich) Results: We have simulated TTFields distribution of 317 patients treated with TTFields as part of the EF-14 trial. Our method is optimized for accurate contouring of tissues highly influencing the distribution of electric field (Scalp, skull, CSF, ventricles), and it is robust, having the ability to give sufficient results even when MRI data quality is low. Thus enabling a study correlating the spatial distribution of TTFields and patient outcome. Conclusions: Our process for rapidly creating patient presents a breakthrough that enables the first study in which the spatial distribution of therapeutic electric fields correlates with patient outcome. In the future this method can be used for clinical studies investigating other clinical indications of large datasets.