Selecting electrode configurations for image-guided cochlear implant programming using template matching

Evaluation of CI electrode position from imaging: comparison of an automated technique with the established manual method

BACKGROUND

A manual evaluation of the CI electrode position from CT and DVT scans may be affected by diagnostic errors due to cognitive biases. The aim of this study was to compare the CI electrode localization using an automated method (image-guided cochlear implant programming, IGCIP) with the clinically established manual method.

METHODS

This prospective experimental study was conducted on a dataset comprising N=50 subjects undergoing cochlear implantation with a Nucleus® CI532 or CI632 Slim Modiolar electrode. Scalar localization, electrode-to-modiolar axis distances (EMD) and angular insertion depth (aDOI) were compared between the automated IGCIP tool and the manual method. Two raters made the manual measurements, and the interrater reliability (±1.96·SD) was determined as the reference for the method comparison. The method comparison was performed using a correlation analysis and a Bland-Altman analysis.

RESULTS

Concerning the scalar localization, all electrodes were localized both manually and automatically in the scala tympani. The interrater differences ranged between ±0.2 mm (EMD) and ±10° (aDOI). There was a bias between the automatic and manual method in measuring both localization parameters, which on the one hand was smaller than the interrater variations. On the other hand, this bias depended on the magnitude of the EMD respectively aDOI. A post-hoc analysis revealed that the deviations between the methods were likely due to a different selection of mid-modiolar axis.

CONCLUSIONS

The IGCIP is a promising tool for automated processing of CT and DVT scans and has useful functionality such as being able to segment the cochlear using post-operative scans. When measuring EMD, the IGCIP tool is superior to the manual method because the smallest possible distance to the axis is determined depending on the cochlear turn, whereas the manual method selects the helicotrema as the reference point rigidly. Functionality to deal with motion artifacts and measurements of aDOI according to the consensus approach are necessary, otherwise the IGCIP is not unrestrictedly ready for clinical use.

A Graph-Based Method for Optimal Active Electrode Selection in Cochlear Implants

The cochlear implant (CI) is a neural prosthetic that is the standard-of-care treatment for severe-to-profound hearing loss. CIs consist of an electrode array inserted into the cochlea that electrically stimulates auditory nerve fibers to induce the sensation of hearing. Competing stimuli occur when multiple electrodes stimulate the same neural pathways. This is known to negatively impact hearing outcomes. Previous research has shown that image-processing techniques can be used to analyze the CI position in CT scans to estimate the degree of competition between electrodes based on the CI user's unique anatomy and electrode placement. The resulting data permits an algorithm or expert to select a subset of electrodes to keep active to alleviate competition. Expert selection of electrodes using this data has been shown in clinical studies to lead to significantly improved hearing outcomes for CI users. Currently, we aim to translate these techniques to a system designed for worldwide clinical use, which mandates that the selection of active electrodes be automated by robust algorithms. Previously proposed techniques produce optimal plans with only 48% success rate. In this work, we propose a new graph-based approach. We design a graph with nodes that represent electrodes and edge weights that encode competition between electrode pairs. We then find an optimal path through this graph to determine the active electrode set. Our method produces results judged by an expert to be optimal in over 95% of cases. This technique could facilitate widespread clinical translation of image-guided cochlear implant programming methods.

Automatic graph-based method for localization of cochlear implant electrode arrays in clinical CT with sub-voxel accuracy

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 up-sampled 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 0.12 mm when compared to expert manual localization results. This represents a crucial step in translating IGCIP from the laboratory to large-scale clinical use.

Automatic localization of closely spaced cochlear implant electrode arrays in clinical CTs

PURPOSE

Cochlear implants (CIs) are neural prosthetic devices that provide a sense of sound to people who experience profound hearing loss. Recent research has indicated that there is a significant correlation between hearing outcomes and the intracochlear locations of the electrodes. We have developed an image-guided cochlear implant programming (IGCIP) system based on this correlation to assist audiologists with programming CI devices. One crucial step in our IGCIP system is the localization of CI electrodes in postimplantation CTs. Existing methods for this step are either not fully automated or not robust. When the CI electrodes are closely spaced, it is more difficult to identify individual electrodes because there is no intensity contrast between them in a clinical CT. The goal of this work is to automatically segment the closely spaced CI electrode arrays in postimplantation clinical CTs.

METHODS

The proposed method involves firstly identifying a bounding box that contains the cochlea by using a reference CT. Then, the intensity image and the vesselness response of the VOI are used to segment the regions of interest (ROIs) that may contain the electrode arrays. For each ROI, we apply a voxel thinning method to generate the medial axis line. We exhaustively search through all the possible connections of medial axis lines. For each possible connection, we define CI array centerline candidates by selecting two points on the connected medial axis lines as the array endpoints. For each CI array centerline candidate, we use a cost function to evaluate its quality, and the one with the lowest cost is selected as the array centerline. Then, we fit an a priori known geometric model of the array to the centerline to localize the individual electrodes. The method was trained on 28 clinical CTs of CI recipients implanted with three models of closely spaced CI arrays. The localization results are compared with the ground truth localization results manually generated by an expert.

RESULTS

A validation study was conducted on 129 clinical CTs of CI recipients implanted with three models of closely spaced arrays. Ninety-eight percent of the localization results generated by the proposed method had maximum localization errors lower than one voxel diagonal of the CTs. The mean localization error was 0.13 mm, which was close to the rater's consistency error (0.11 mm). The method also outperformed the existing automatic electrode localization methods in our validation study.

CONCLUSION

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.