Cochlear implant electrode sequence optimization using patient specific neural stimulation models

Ziteng Liu; Ahmet Cakir; Jack H. Noble

doi:10.1117/12.2550578

16 March 2020 Cochlear implant electrode sequence optimization using patient specific neural stimulation models

Ziteng Liu, Ahmet Cakir, Jack H. Noble

Author Affiliations +

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 113152V (2020) https://doi.org/10.1117/12.2550578
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Cochlear implants (CIs) use an array of electrodes implanted in the cochlea to directly stimulate the auditory nerve. After surgery, CI recipients undergo many programming sessions with an audiologist who adjusts CI processor settings to improve performance. However, few tools exist to help audiologists know what settings will lead to better performance. In this paper, we propose a new method to assist audiologists by determining a customized firing order of the electrodes on the array using image-based models of patient specific neural stimulation patterns. Our models permit estimating the time delay needed after firing an electrode so that the nerve fibers they stimulate can recover from the refractory period. These predictions allow us to design an optimization algorithm that determines a customized electrode firing order that minimizes negative effects of overlapping stimulation between electrodes. The customized order reduces how often nerves that are in a refractory state from previous stimulation by one electrode are targeted for activation by a subsequent electrode in the sequence. Our experiments show that this method is able to reduce the theoretical stimulation overlap artifacts and could lead to improved hearing outcomes for CI recipients.

Citation Download Citation

Ziteng Liu, Ahmet Cakir, and Jack H. Noble "Cochlear implant electrode sequence optimization using patient specific neural stimulation models", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113152V (16 March 2020); https://doi.org/10.1117/12.2550578

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