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Proceedings Paper

Validation of a metal artifact reduction method based on 3D conditional GANs for CT images of the ear
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Paper Abstract

Cochlear implants (CIs) are surgically implanted neural prosthetic devices used to treat severe-to-profound hearing loss. Our group has developed Image-Guided Cochlear Implant Programming (IGCIP) techniques to assist audiologists with the configuration of the implanted CI electrodes. CI programming is sensitive to the spatial relationship between the electrodes and intra cochlear anatomy (ICA) structures. We have developed algorithms that permit determining the position of the electrodes relative to the ICA structure using pre- and post-implantation CT image pairs. However, these do not extend to CI recipients for whom pre-implantation CT (Pre-CT) images are not available because post-implantation CT (Post-CT) images are affected by strong artifacts introduced by the metallic implant. Recently, we proposed an approach that uses conditional generative adversarial nets (cGANs) to synthesize Pre-CT images from Post-CT images. This permits to use algorithms designed to segment Pre-CT images even when these are not available. We have shown that it substantially and significantly improves the results obtained with our previous published methods that segment post- CT images directly. Here we evaluate the effect of this new approach on the final output of our IGCIP techniques, which is the configuration of the CI electrodes, by comparing configurations of the CI electrodes obtained using the real and the synthetic Pre-CT images. In 22/87 cases synthetic image lead to the same results as the real images. Because more than one configuration may lead to equivalent neural stimulation patterns, visual assessment of solutions is required to compare those that differ. This study is ongoing.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 1131507 (16 March 2020); doi: 10.1117/12.2549398
Show Author Affiliations
Jianing Wang, Vanderbilt Univ. (United States)
Srijata Chakravorti, Vanderbilt Univ. (United States)
Yiyuan Zhao, Siemens Medical Solutions (United States)
Jack H. Noble, Vanderbilt Univ. (United States)
Benoit M. Dawant, Vanderbilt Univ. (United States)


Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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