We propose an approach based on a conditional generative adversarial network (cGAN) for the reduction of metal artifacts (RMA) in computed tomography (CT) ear images of cochlear implants (CIs) recipients. Our training set contains paired pre-implantation and post-implantation CTs of 90 ears. At the training phase, the cGAN learns a mapping from the artifact-affected CTs to the artifact-free CTs. At the inference phase, given new metal-artifact-affected CTs, the cGAN produces CTs in which the artifacts are removed. As a pre-processing step, we also propose a band-wise normalization method, which splits a CT image into three channels according to the intensity value of each voxel and we show that this method improves the performance of the cGAN. We test our cGAN on post-implantation CTs of 74 ears and the quality of the artifact-corrected images is evaluated quantitatively by comparing the segmentations of intra-cochlear anatomical structures, which are obtained with a previously published method, in the real pre-implantation and the artifact-corrected CTs. We show that the proposed method leads to an average surface error of 0.18 mm which is about half of what could be achieved with a previously proposed technique.

Cochlear Implants (CI) are surgically implanted neural prosthetic devices used to treat severe-toprofound hearing loss. Recent studies have suggested that hearing outcomes with CIs are correlated with the location where individual electrodes in the implanted electrode array are placed, but techniques proposed for determining electrode location have been too coarse and labor intensive to permit detailed analysis on large numbers of datasets. In this paper, we present a fully automatic snake-based method for accurately localizing CI electrodes in clinical post-implantation CTs. Our results show that average electrode localization errors with the method are 0.21millimeters. These results indicate that our method could be used in future large scale studies to analyze the relationship between electrode position and hearing outcome, which potentially could lead to technological advances that improve hearing outcomes with CIs.

Cochlear implants (CIs) are neural prosthetics that provide a sense of sound to people who experience severe to profound hearing loss. Recent studies have demonstrated a correlation between hearing outcomes and intra-cochlear locations of CI electrodes. Our group has been conducting investigations on this correlation and has been developing an image-guided cochlear implant programming (IGCIP) system to program CI devices to improve hearing outcomes. One crucial step that has not been automated in IGCIP is the localization of CI electrodes in clinical CTs. Existing methods for CI electrode localization do not generalize well on large-scale datasets of clinical CTs implanted with different brands of CI arrays. In this paper, we propose a novel method for localizing different brands of CI electrodes in clinical CTs. We firstly generate the candidate electrode positions at sub-voxel resolution in a whole head CT by thresholding an upsampled feature image and voxel-thinning the result. Then, we use a graph-based path-finding algorithm to find a fixed-length path that consists of a subset of the candidates as the localization result. Validation on a large-scale dataset of clinical CTs shows that our proposed method outperforms the state-of-art CI electrode localization methods and achieves a mean error of 0.12mm when compared to expert manual localization results. This represents a crucial step in translating IGCIP from the laboratory to large-scale clinical use.

Our validation study shows that our method can localize closely spaced CI arrays with an accuracy close to what is achievable by an expert on clinical CTs. This represents a crucial step toward automating IGCIP and translating it from the laboratory to the clinical workflow.

Abstract. Cochlear implants (CIs) are neural prostheses that restore hearing by stimulating auditory nerve pathways within the cochlea using an implanted electrode array. Research has shown when multiple electrodes stimulate the same nerve pathways, competing stimulation occurs and hearing outcomes decline. Recent clinical studies have indicated that hearing outcomes can be significantly improved by using an image-guided active electrode set selection technique we have designed, in which electrodes that cause competing stimulation are identified and deactivated. In tests done to date, an expert is needed to perform the electrode selection step with the assistance of a method to visualize the spatial relationship between electrodes and neural sites determined using image analysis techniques. We propose to automate the electrode selection step by optimizing a cost function that captures the heuristics used by the expert. Further, we propose an approach to estimate the values of parameters used in the cost function using an existing database of expert electrode selections. We test this method with different electrode array models from three manufacturers. Our automatic approach generates acceptable active electrode sets in 98.3% of the subjects tested. This approach represents a crucial step toward clinical translation of our image-guided CI programming system.