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3 March 2017 A cochlear implant phantom for evaluating CT acquisition parameters
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Cochlear Implants (CIs) are surgically implantable neural prosthetic devices used to treat profound hearing loss. Recent literature indicates that there is a correlation between the positioning of the electrode array within the cochlea and the ultimate hearing outcome of the patient, indicating that further studies aimed at better understanding the relationship between electrode position and outcomes could have significant implications for future surgical techniques, array design, and processor programming methods. Post-implantation high resolution CT imaging is the best modality for localizing electrodes and provides the resolution necessary to visually identify electrode position, albeit with an unknown degree of accuracy depending on image acquisition parameters, like the HU range of reconstruction, radiation dose, and resolution of the image. In this paper, we report on the development of a phantom that will both permit studying which CT acquisition parameters are best for accurately identifying electrode position and serve as a ground truth for evaluating how different electrode localization methods perform when using different CT scanners and acquisition parameters. We conclude based on our tests that image resolution and HU range of reconstruction strongly affect how accurately the true position of the electrode array can be found by both experts and automatic analysis techniques. The results presented in this paper demonstrate that our phantom is a versatile tool for assessing how CT acquisition parameters affect the localization of CIs.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Srijata Chakravorti, Brian J. Bussey, Yiyuan Zhao, Benoit M. Dawant, Robert F. Labadie, and Jack H. Noble "A cochlear implant phantom for evaluating CT acquisition parameters", Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101350N (3 March 2017);

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