Image-guidance enables new methods for customizing cochlear implant stimulation strategies

Abstract

Over the last 20 years, cochlear implants (CIs) have become what is arguably the most successful neural prosthesis to date. Despite this success, a significant number of CI recipients experience marginal hearing restoration, and, even among the best performers, restoration to normal fidelity is rare. In this paper, we present image processing techniques that can be used to detect, for the first time, the positions of implanted CI electrodes and the nerves they stimulate for individual CI users. These techniques permit development of new, customized CI stimulation strategies. We present one such strategy and show that it leads to significant hearing improvement in an experiment conducted with 11 CI recipients. These results indicate that image-guidance can be used to improve hearing outcomes for many existing CI recipients without requiring additional surgical procedures.

Fig. 1

Spatial analysis of an implanted subject. The scala tympani (red) and scala vestibuli (blue), the two principal cavities of the cochlea, are shown in (A–C). In (B), also shown is a rendering of the auditory nerve bundles of the SG in green. In (C–D), the AR (the surface representing the interface between the nerves of the SG and the intra-cochlear cavities) is colorcoded with the tonotopic place frequencies of the SG in Hz. Also shown in (D) are the implanted electrodes of the CI, numbered 1–12. An illustration of current spread from each electrode is rendered transparently in red and blue, with the color alternating between neighboring electrodes. Electrode distance-vs.-frequency curves, shown as a sequence of blue and red segments, are plotted in (E), which corresponds to the ear shown in (D), and (F), which is the curve for the better performing contra-lateral ear of the same subject.

Fig. 2

Shown in (a) and (b) are a slice of a μCT and a CT of a human cochlea. In (c) and (d), the scala tympani (red), scala vestibuli (blue), and bundle of nerve cells of the SG (green) are delineated in the same slice.

Fig. 3

Segmentation error distributions of Dice similarity scores for the whole SG (WSG) and mean and max symmetric surface error distributions for the WSG and in the active region (AR).

Fig. 4

Automatic (top row) and manual (bottom row) segmentations of the active region of the SG in the 5 test volumes (left-to-right) color encoded with error distance (mm).

Fig. 5

Delineations of the automatic (red/blue) and manual (green) segmentation of the SG in the CT (a) and μCT (b) slice from Figure 2. The active region is shown in blue.

Fig. 6

Electrode distance-vs.-frequency curves for each test subject. (A)–(I) Show the electrode distance-vs.-frequency curves, similarly to Figure 1e, for subjects 2–10. Electrodes deactivated in our experiments are shown in red.

Fig. 7

Box plots of the difference between post- and pre-adjustment scores of each hearing performance measure. Shown are the interquartile range (box), median (red line), individual data points (black dots). Whiskers extend to data points that lie within 2 times the interquartile range from the mean. Outlier points that lie beyond the whiskers are highlighted in red. Measures that reach statistical significance are indicated by red circles.

Fig. 8

Individual hearing performance results for all ten subjects shown as line plots. The left and right ends of each line plot show pre- and post-adjustment results for the indicated subject and hearing performance measure, respectively.