Cochlear implant electric field estimation using 3D neural networks

Ziteng Liu; Jack H. Noble

doi:10.1117/12.2582036

15 February 2021 Cochlear implant electric field estimation using 3D neural networks

Ziteng Liu, Jack H. Noble

Author Affiliations +

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 115981E (2021) https://doi.org/10.1117/12.2582036
Event: SPIE Medical Imaging, 2021, Online Only

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 order to provide objective information to the audiologist for programming, our group has developed a system to permit estimating which auditory neural sites are stimulated by which CI electrodes. To do this, we have proposed physics-based models to calculate the electric field in the cochlea generated by electrical stimulation. However, solving these models require days of computation time and substantial computational resources. In this paper, we proposed a deep-learningbased method to estimate the patient-specific electric fields using a U-Net-like architecture with physics-based loss function. Our network is trained with a dataset generated by solving physics-based models and the results show that the proposed method can achieve similar accuracy with traditional method and largely improves the speed of estimating the intra-cochlear electric field.

Conference Presentation

Citation Download Citation

Ziteng Liu and Jack H. Noble "Cochlear implant electric field estimation using 3D neural networks", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115981E (15 February 2021); https://doi.org/10.1117/12.2582036

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