Evaluation of a high-resolution patient-specific model of the electrically stimulated cochlea

Cochlear implants (CIs) are surgically implanted medical devices used to treat individuals with severe-to-profound sensorineural hearing loss. Although these devices have been remarkably successful at restoring audibility, many patients experience poor outcomes. Our group has developed the first image-guided CI programming technique where the electrode positions are found in CT images and used to estimate neural activation patterns, which is unique information that audiologists can use to define patient-specific processor settings. Currently, neural activation is estimated using only the distance from each electrode to the neural activation sites, which might be less accurate than using high-resolution electro-anatomical models (EAMs) to perform physics-based estimations of neural activation. We propose a patient-customized EAM approach where the EAM is spatially and electrically adapted to a patient-specific configuration. Spatial adaptation is done through nonrigid registration of the model with the patient CT image. Electrical adaptation is done by adjusting tissue resistivity parameters, so the intracochlear voltage distributions predicted by the model best match those directly measured for the patient via their implant. We found that our approach, demonstrated for [Formula: see text] patients, results in mean percent differences between direct and simulated measurements of voltage distributions of 10.9%.