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Sensitivity Analysis Highlights the Importance of Accurate Head Models for Electrical Impedance Tomography Monitoring of Intracerebral Hemorrhagic Stroke

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Stroke is the second leading cause of death worldwide, causing 5.5 million deaths annually. Up to 50% of stroke survivors are permanently disabled. While hemorrhagic stroke represents only a minority of the cases, it carries significantly higher mortality rates than ischemic stroke. Repeated bleeding and hematoma expansion are common complications resulting in poor outcomes and increased mortality.

Electrical impedance tomography (EIT) has been proposed as a novel tool for diagnosing and monitoring stroke. However, so far, the clinical feasibility is unresolved. Possible issue could be the relatively low quality of volume conductor models used. In this study, we investigate the role of accurate head modeling in EIT and how the inhomogeneities of the head contribute to the EIT measurement and affect its feasibility in monitoring the progression of a hemorrhagic stroke.

We compared anatomically detailed six- and three-layer finite element models of a human head and computed the resulting scalp electrode potentials and the lead fields of selected electrode configurations. We visualized the resulting EIT measurement sensitivity distributions, computed the scalp electrode potentials, and examined the inverse imaging with selected cases. The effect of accurate tissue geometry and conductivity values on the EIT measurement is assessed with multiple different hemorrhagic perturbation locations and sizes.

Our results show that accurate tissue geometries and conductivity values inside the cranial cavity, especially the highly conductive cerebral spinal fluid (CSF), significantly affect EIT measurement sensitivity distribution and measured potentials. This effected the inverse imaging of the selected test cases.

We can conclude that the three-layer head models commonly used in EIT literature cannot depict the current paths correctly in the head. Thus, our study highlights the need to consider the detailed geometry of CSF in EIT. The results show that the CSF should be considered in computational models of EIT stroke monitoring.

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