Toward more reliable testing of cochlear implant electrodes: A method for image-based alignment of cochlear specimens

Introduction 

Insertion tests into ex vivo cochlear specimens are a widely used technique for the experimental evaluation of cochlear implant electrode arrays (EA) whereby forces are measured and analyzed to characterize the EAs. Since the orientation of the cochlear lumen with respect to the insertion axis of the test setup strongly effects these forces, a controlled alignment of the specimen needs to be ensured. So far, the alignment of the cochlear specimens can only be roughly estimated based on anatomical landmarks.

Material and Methods 

To overcome this limitation we developed a new alignment method, using image-guidance and 3D-printing of an individual pose-setting adapter (PSA). The method contains the fixation of the specimen on top of a custom registration and specimen carrier (RSC), CBCT-scan of the specimen and image-based planning of the insertion trajectory. After calculation and 3D-printing of the individual PSA, it is mounted between the force sensor and the RSC to adjust the specimens pose. For a proof of concept, three porcine cochlear specimens were positioned, followed by automated EA-insertion (0.1 mm/s) of a commercially available EA.

Results 

Mean positioning accuracy of the new method was found to be  ≤ 0.23 mm and  ≤ 0.38° measured as the deviation between the actual pose of the specimen and the insertion axis of the test setup. Furthermore, the EA could be successfully inserted into all specimens, with full insertion possible in one out of three cases and up to 10 resp. 11 of 12 contacts in the remaining cases.

Discussion 

The presented alignment method is characterized by a high level of reproducibility due to image-guided planning and positioning. With the simple integration into test setups, insertion tests can be performed more standardized in future.

Federal Ministry of Education and Research of Germany (BMBF, grant number 13GW0367B); Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2177/1 – Project ID 390895286.

Publication History

Article published online:
24 May 2022