Localization of Cochlear Implant Electrodes from Cone Beam Computed Tomography using Particle Belief Propagation

TL;DR

This paper introduces a novel MRF-based method using particle belief propagation for precise localization of cochlear implant electrodes in CBCT scans, significantly improving accuracy over existing techniques.

Contribution

The paper presents a new MRF model incorporating electrode intensity, shape, and spatial relations, with a particle belief propagation inference method for cochlear implant electrode localization.

Findings

Up to 31.5% increase in localization precision on real CBCT data

Outperforms two state-of-the-art algorithms in accuracy

Validated on synthetic and real datasets

Abstract

Cochlear implants (CIs) are implantable medical devices that can restore the hearing sense of people suffering from profound hearing loss. The CI uses a set of electrode contacts placed inside the cochlea to stimulate the auditory nerve with current pulses. The exact location of these electrodes may be an important parameter to improve and predict the performance with these devices. Currently the methods used in clinics to characterize the geometry of the cochlea as well as to estimate the electrode positions are manual, error-prone and time consuming. We propose a Markov random field (MRF) model for CI electrode localization for cone beam computed tomography (CBCT) data-sets. Intensity and shape of electrodes are included as prior knowledge as well as distance and angles between contacts. MRF inference is based on slice sampling particle belief propagation and guided by several heuristics. A stochastic search finds the best maximum a posteriori estimation among sampled MRF realizations. We evaluate our algorithm on synthetic and real CBCT data-sets and compare its performance with two state of the art algorithms. An increase of localization precision up to 31.5% (mean), or 48.6% (median) respectively, on real CBCT data-sets is shown.