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Validation of a Cochlear Implant Patient-Specific Model of the Voltage Distribution in a Clinical Setting

View Article: PubMed Central - PubMed

ABSTRACT

Cochlear Implants (CIs) are medical implantable devices that can restore the sense of hearing in people with profound hearing loss. Clinical trials assessing speech intelligibility in CI users have found large intersubject variability. One possibility to explain the variability is the individual differences in the interface created between electrodes of the CI and the auditory nerve. In order to understand the variability, models of the voltage distribution of the electrically stimulated cochlea may be useful. With this purpose in mind, we developed a parametric model that can be adapted to each CI user based on landmarks from individual cone beam computed tomography (CBCT) scans of the cochlea before and after implantation. The conductivity values of each cochlea compartment as well as the weighting factors of different grounding modes have also been parameterized. Simulations were performed modeling the cochlea and electrode positions of 12 CI users. Three models were compared with different levels of detail: a homogeneous model (HM), a non-patient-specific model (NPSM), and a patient-specific model (PSM). The model simulations were compared with voltage distribution measurements obtained from the backward telemetry of the 12 CI users. Results show that the PSM produces the lowest error when predicting individual voltage distributions. Given a patient-specific geometry and electrode positions, we show an example on how to optimize the parameters of the model and how to couple it to an auditory nerve model. The model here presented may help to understand speech performance variability and support the development of new sound coding strategies for CIs.

No MeSH data available.


(A) Postoperative CBCT scan with the electrode positions. (B) CAD model of a Nucleus contour advance electrode array. The red color represents the silicon electrode carrier, the white color represents the electrode contacts, and the yellow color represents the different structures of the cochlea.
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Figure 5: (A) Postoperative CBCT scan with the electrode positions. (B) CAD model of a Nucleus contour advance electrode array. The red color represents the silicon electrode carrier, the white color represents the electrode contacts, and the yellow color represents the different structures of the cochlea.

Mentions: A method was developed to improve the estimates of the electrode positions. The estimate is challenging due to the artifacts present in the postoperative scans and the relatively low resolution of the CBCT scanner (voxel size: 0.3 mm × 0.3 mm × 0.3 mm) in comparison to an average electrode spacing of less than around 1 mm. First, the estimates were smoothed fitting a spline through the 22 electrode positions in the CAD coordinate system. Second, the most apical electrode was used as the reference position, and all other electrode position estimates were corrected using the known dimension of the CI24RE or the CI422 Slim-Straight electrode array. Figure 5A presents a postoperative CBCT image with the electrode positions estimated using the method described above marked with green color.


Validation of a Cochlear Implant Patient-Specific Model of the Voltage Distribution in a Clinical Setting
(A) Postoperative CBCT scan with the electrode positions. (B) CAD model of a Nucleus contour advance electrode array. The red color represents the silicon electrode carrier, the white color represents the electrode contacts, and the yellow color represents the different structures of the cochlea.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120131&req=5

Figure 5: (A) Postoperative CBCT scan with the electrode positions. (B) CAD model of a Nucleus contour advance electrode array. The red color represents the silicon electrode carrier, the white color represents the electrode contacts, and the yellow color represents the different structures of the cochlea.
Mentions: A method was developed to improve the estimates of the electrode positions. The estimate is challenging due to the artifacts present in the postoperative scans and the relatively low resolution of the CBCT scanner (voxel size: 0.3 mm × 0.3 mm × 0.3 mm) in comparison to an average electrode spacing of less than around 1 mm. First, the estimates were smoothed fitting a spline through the 22 electrode positions in the CAD coordinate system. Second, the most apical electrode was used as the reference position, and all other electrode position estimates were corrected using the known dimension of the CI24RE or the CI422 Slim-Straight electrode array. Figure 5A presents a postoperative CBCT image with the electrode positions estimated using the method described above marked with green color.

View Article: PubMed Central - PubMed

ABSTRACT

Cochlear Implants (CIs) are medical implantable devices that can restore the sense of hearing in people with profound hearing loss. Clinical trials assessing speech intelligibility in CI users have found large intersubject variability. One possibility to explain the variability is the individual differences in the interface created between electrodes of the CI and the auditory nerve. In order to understand the variability, models of the voltage distribution of the electrically stimulated cochlea may be useful. With this purpose in mind, we developed a parametric model that can be adapted to each CI user based on landmarks from individual cone beam computed tomography (CBCT) scans of the cochlea before and after implantation. The conductivity values of each cochlea compartment as well as the weighting factors of different grounding modes have also been parameterized. Simulations were performed modeling the cochlea and electrode positions of 12 CI users. Three models were compared with different levels of detail: a homogeneous model (HM), a non-patient-specific model (NPSM), and a patient-specific model (PSM). The model simulations were compared with voltage distribution measurements obtained from the backward telemetry of the 12 CI users. Results show that the PSM produces the lowest error when predicting individual voltage distributions. Given a patient-specific geometry and electrode positions, we show an example on how to optimize the parameters of the model and how to couple it to an auditory nerve model. The model here presented may help to understand speech performance variability and support the development of new sound coding strategies for CIs.

No MeSH data available.