The goals of this research are to develop and evaluate new patient-customized, Image-Guided Cochlear Implant Programming (IGCIP) strategies. With over 320,000 recipients worldwide, cochlear implants (CIs) are considered standard of care treatment for severe-to-profound sensory-based hearing loss. The programming process that audiologists use clinically is one factor that limits outcomes because, while existing devices permit manipulation of very many settings that could lead to better performance, there are no objective cues available to indicate what setting changes will lead to better performance. Any advancement that accelerates convergence to settings that better approximate natural fidelity could have significant impact for CI recipients, clinicians, and audiology centers. The goal of this project is to develop and evaluate new IGCIP strategies that could provide object information to the programming process and lead to programs that better approximate natural fidelity. In natural hearing, each neural fiber (out of ~30,000) is activated when its characteristic frequency (CF) is present in a sound. With a CI, due to the small number of electrodes (12 to 22), their large size relative to the individual nerves, and their wide curren spread, limited spectral resolution has been achievable, thus each electrode stimulates nerves corresponding to a wide range of CFs. Since this is generally not accounted for in traditional programming, sub-optimal settings are typically chosen that result in interacting channels, causing spectral smearing artifacts. Further, since the stimulation patterns of the electrodes are unknown, the CFs stimulated by each electrode are unknown. Thus the sound frequencies assigned to each electrode do not generally correspond to the CFs of the nerves it stimulates, resulting in frequency mismatch artifacts. These limitations negatively affect outcomes and, while known, have been difficult to address. The hypothesis of this study is that more objective, important information can be obtained through analysis of patient CT images and can be used to customize CI settings for improved hearing performance. The IGCIP strategies that will be tested will involve using imaging to detect where spectral smearing and frequency mismatching is occurring and to minimize these artifacts through selection of patient-customized program settings, including frequency table settings, current steering settings, and current focusing settings. To support the design of IGCIP strategies, an approach for using patient CT images to create patient-specific, comprehensive models of electrical current flow and the CI's neural activation patterns will also be developed. Since IGCIP strategies require only simple changes of CI settings, they work with existing device technology, do not require further surgery, and are reversible. If successful, a suite of IGCIP techniques that can objectively guide the programming of CIs towards optimized settings and improve hearing restoration for new and existing CI recipients will be developed in this project.

Public Health Relevance

The proposed project involves developing cochlear implant programming-assistance systems that could provide useful information to clinicians and lead to better device settings. Thus, the methods developed in this project could potentially be used to improve hearing outcomes for new and existing cochlear implant recipients and improve the efficiency of the clinical programming process.

Agency
National Institute of Health (NIH)
Institute
National Institute on Deafness and Other Communication Disorders (NIDCD)
Type
Research Project (R01)
Project #
1R01DC014037-01
Application #
8752841
Study Section
Special Emphasis Panel (BNVT)
Program Officer
Miller, Roger
Project Start
2014-06-01
Project End
2019-05-31
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
1
Fiscal Year
2014
Total Cost
$388,179
Indirect Cost
$140,931
Name
Vanderbilt University Medical Center
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
004413456
City
Nashville
State
TN
Country
United States
Zip Code
37212
Zhao, Yiyuan; Dawant, Benoit M; Labadie, Robert F et al. (2014) Automatic localization of cochlear implant electrodes in CT. Med Image Comput Comput Assist Interv 17:331-8