gms | German Medical Science

70. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC)
Joint Meeting mit der Skandinavischen Gesellschaft für Neurochirurgie

Deutsche Gesellschaft für Neurochirurgie (DGNC) e. V.

12.05. - 15.05.2019, Würzburg

Simulation of TTFields distribution within patient-specific computational head models

Simulation der TTFields-Verteilung in patientenspezifischen Kopfmodellen

Meeting Abstract

  • Adrian Kinzel - Novocure GmbH, München, Deutschland
  • Noa Urman - Novocure, Haifa, Israel
  • Shay Levi - Novocure, Haifa, Israel
  • Ariel Naveh - Novocure, Haifa, Israel
  • Doron Manzur - Novocure, Haifa, Israel
  • Hadas Sara Hershkovich - Novocure, Haifa, Israel
  • Eilon Kirson - Novocure, Haifa, Israel
  • presenting/speaker Zeev Bomzon - Novocure, Haifa, Israel

Deutsche Gesellschaft für Neurochirurgie. 70. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), Joint Meeting mit der Skandinavischen Gesellschaft für Neurochirurgie. Würzburg, 12.-15.05.2019. Düsseldorf: German Medical Science GMS Publishing House; 2019. DocP202

doi: 10.3205/19dgnc538, urn:nbn:de:0183-19dgnc5387

Published: May 8, 2019

© 2019 Kinzel et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at



Objective: The distribution of TTFields in the brain is dependent on the patient anatomy, the position of the arrays as well as electric properties of the tissues and tumor. Our goal is to develop realistic computational patient models that allow understanding the effect of field distribution influences progression of disease. Preparing such models is very time intensive and in clinical scenarios, MRI acquisition time is often reduced by increased slice spacing, limited field of view, or increased scan speed, leading to difficulties in creating automated models. Our method enables model creation under those restrictions. The model uses a realistic head model of a healthy person used as a deformable template with which the patient model is derived.

Methods: We used a highly detailed healthy head model that served as deformable template from which patient models are created. Pre-processing is the first step and involves denoising and background noise reduction in addition to super-resolution algorithms as needed. For development of 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 Elements Method (Sim4Life V3.0, ZMT-Zurich).

Results: TTFields distribution of 317 patients treated with TTFields in the EF-14 trial were simulated. The method enables accurate contouring of tissues highly influencing the distribution of electric field (Scalp, skull, CSF, ventricles). This makes possible a study correlating the spatial distribution of TTFields and patient outcome.

Conclusion: We developed a process for rapidly creating patient models that enables the first study on spatial distribution of therapeutic electric fields and correlation with patient outcome.