Simplified realistic human head model for simulating Tumor Treating Fields (TTFields).

Area of Science:

• Medical Physics
• Computational Electromagnetics
• Neuro-oncology

Background:

• Tumor Treating Fields (TTFields) are an approved anti-mitotic therapy for Glioblastoma Multiforme (GBM).
• Accurate delivery of TTFields depends on optimal transducer array placement, guided by computational models.
• Current methods for creating personalized head models are complex and time-consuming due to segmentation challenges.

Purpose of the Study:

• To develop a simplified, efficient pipeline for creating personalized head models for TTFields therapy.
• To evaluate the accuracy of simplified head models in predicting electric field distribution compared to realistic models.

Main Methods:

• Creation of simplified head models using convex hulls of tissue layers.
• Incorporation of anisotropic conductivity in cortical tissues using Diffusion Tensor Imaging (DTI).
• Comparison of induced electric field distribution between simplified and realistic head models.

Main Results:

• Simplified head models provide a fast and efficient alternative to realistic models.
• Average field intensities in the brain and tumor were slightly higher in realistic models (max 114% ratio).
• The proposed pipeline enables accurate predictions of electric field distribution with reduced complexity.

Conclusions:

• Simplified head models offer a viable approach for personalized TTFields therapy planning.
• This method reduces the complexity of characterizing tissue interfaces for computational modeling.
• The pipeline facilitates faster and more accessible personalized treatment optimization for GBM patients.