Cochlear implants consist of an implanted array of electrodes and passive electronics and an external processor designed to directly stimulate auditory nerve fibers to induce the sensation of hearing in those who have experienced hearing loss caused by problems in the inner ear. After surgical implantation, audiologists program the processor with settings intended to produce optimal hearing outcomes. The likelihood of optimal outcomes increases when audiologists are provided with tools that assist them in making objective decisions based on the patient’s own anatomy and surgical placement of the array. A visualization tool currently in use, called distance-versus-frequency (DVF)curves, can be used to estimate channel interactions between electrodes. Although the information presented is objective, an audiologist’s decisions are subject to their own estimations applied to this visualization. In this paper, we present a new visualization technique designed to remove the subjectivity of visually assessing channel interactions between electrodes. Preliminary results show plans generated using this method are more consistent and are rated as optimal more often than those generated using DVF curves.
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 N=7 patients, results in mean percent differences between direct and simulated measurements of voltage distributions of 10.9%.
Cochlear implants (CIs) are considered standard treatment for patients who experience sensorineural hearing loss. Although these devices have been remarkably successful at restoring hearing, it is rare to achieve natural fidelity, and many patients experience poor outcomes. Our group has developed the first image-guided CI programming (IGCIP) technique where the positions of the electrodes 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. In our current system, neural activation is estimated using only the distance from each electrode to the neural activation sites. This approach might be less accurate than using a high-resolution electro-anatomical model (EAM) of the electrically stimulated cochlea to perform physics-based estimation of neural activation. In this work, we propose a patientcustomized EAM approach where the EAM is spatially and electrically adapted to a patient-specific configuration. Spatial adaptation is done through non-rigid registration of the model with the patient CT image. Electrical adaptation is done by adjusting tissue resistivity parameters so that the intra-cochlear voltage distributions predicted by the model best match those directly measured for the patient via their implant. We demonstrated our approach for N=7 patients. We found that our approach results in mean percent differences between direct and simulated measurements of voltage distributions of 11%. In addition, visual comparison shows the simulated and measured voltage distributions are qualitatively in good agreement. This represents a crucial step toward developing and validating the first in vivo patient-specific cochlea EAMs.
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