title: Simplified realistic human head model for simulating Tumor Treating Fields (TTFields).
abstract: Tumor Treating Fields (TTFields) are alternating electric fields in the intermediate frequency range (100-300 kHz) of low-intensity (1-3 V/cm). TTFields are an anti-mitotic treatment against solid tumors, which are approved for Glioblastoma Multiforme (GBM) patients. These electric fields are induced non-invasively by transducer arrays placed directly on the patient's scalp. Cell culture experiments showed that treatment efficacy is dependent on the induced field intensity. In clinical practice, a software called NovoTalTM uses head measurements to estimate the optimal array placement to maximize the electric field delivery to the tumor. Computational studies predict an increase in the tumor's electric field strength when adapting transducer arrays to its location. Ideally, a personalized head model could be created for each patient, to calculate the electric field distribution for the specific situation. Thus, the optimal transducer layout could be inferred from field calculation rather than distance measurements. Nonetheless, creating realistic head models of patients is time-consuming and often needs user interaction, because automated image segmentation is prone to failure. This study presents a first approach to creating simplified head models consisting of convex hulls of the tissue layers. The model is able to account for anisotropic conductivity in the cortical tissues by using a tensor representation estimated from Diffusion Tensor Imaging. The induced electric field distribution is compared in the simplified and realistic head models. The average field intensities in the brain and tumor are generally slightly higher in the realistic head model, with a maximal ratio of 114% for a simplified model with reasonable layer thicknesses. Thus, the present pipeline is a fast and efficient means towards personalized head models with less complexity involved in characterizing tissue interfaces, while enabling accurate predictions of electric field distribution.
authors.forename: Cornelia
authors.initials: C
authors.lastname: Wenger
authors.forename: Ze'ev
authors.initials: Z
authors.lastname: Bomzon
authors.forename: Ricardo
authors.initials: R
authors.lastname: Salvador
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Basser
authors.forename: Pedro C
authors.initials: PC
authors.lastname: Miranda
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging; D001932:Brain Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000628:therapy; D056324:Diffusion Tensor Imaging; D004553:Electric Conductivity; D005260:Female; D005909:Glioblastoma / Q000000981:diagnostic imaging* / Q000473:pathology / Q000628:therapy; D006801:Humans; D008954:Models, Biological; D055815:Young Adult