Uncertainty Quantification of Cochlear Implant Insertion from CT Images

International audienceCochlear implants (CI) are used to treat severe hearing loss by surgically inserting an electrode array into the cochlea. Since current electrodes are designed with various insertion depth, ENT surgeons must choose the implant that will maximise the insertion depth without causing any trauma based on preoperative CT images. In this paper, we propose a novel framework for estimating the insertion depth and its uncertainty from segmented CT images based on a new parametric shape model. Our method relies on the posterior probability estimation of the model parameters using stochastic sampling and a careful evaluation of the model complexity compared to CT and µCT images. The results indicate that preoperative CT images can be used by ENT surgeons to safely select patient-specific cochlear implants

Computational evaluation of cochlear implant surgery outcomes accounting for uncertainty and parameter variability

Cochlear implantation (CI) is a complex surgical procedure that restores hearing in patients with severe deafness. The successful outcome of the implanted device relies on a group of factors, some of them unpredictable or difficult to control. Uncertainties on the electrode array position and the electrical properties of the bone make it difficult to accurately compute the current propagation delivered by the implant and the resulting neural activation. In this context, we use uncertainty quantification methods to explore how these uncertainties propagate through all the stages of CI computational simulations. To this end, we employ an automatic framework, encompassing from the finite element generation of CI models to the assessment of the neural response induced by the implant stimulation. To estimate the confidence intervals of the simulated neural response, we propose two approaches. First, we encode the variability of the cochlear morphology among the population through a statistical shape model. This allows us to generate a population of virtual patients using Monte Carlo sampling and to assign to each of them a set of parameter values according to a statistical distribution. The framework is implemented and parallelized in a High Throughput Computing environment that enables to maximize the available computing resources. Secondly, we perform a patient-specific study to evaluate the computed neural response to seek the optimal post-implantation stimulus levels. Considering a single cochlear morphology, the uncertainty in tissue electrical resistivity and surgical insertion parameters is propagated using the Probabilistic Collocation method, which reduces the number of samples to evaluate. Results show that bone resistivity has the highest influence on CI outcomes. In conjunction with the variability of the cochlear length, worst outcomes are obtained for small cochleae with high resistivity values. However, the effect of the surgical insertion length on the CI outcomes could not be clearly observed, since its impact may be concealed by the other considered parameters. Whereas the Monte Carlo approach implies a high computational cost, Probabilistic Collocation presents a suitable trade-off between precision and computational time. Results suggest that the proposed framework has a great potential to help in both surgical planning decisions and in the audiological setting process

Analysis of uncertainty and variability in finite element computational models for biomedical engineering: characterization and propagation

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering

Model-based prediction of optogenetic sound encoding in the human cochlea by future optical cochlear implants

When hearing fails, electrical cochlear implants (eCIs) partially restore hearing by direct stimulation of spiral ganglion neurons (SGNs). As light can be better confined in space than electrical current, optical CIs (oCIs) provide more spectral information promising a fundamental improvement of hearing restoration by cochlear implants. Here, we turned to computer modelling for predicting the outcome of optogenetic hearing restoration by future oCIs in humans. We combined three-dimensional reconstruction of the human cochlea with ray-tracing simulation of emission from LED or laser-coupled waveguide emitters of the oCI. Irradiance was read out at the somata of SGNs. The irradiance values reached with waveguides were about 14 times higher than with LEDs, at the same radiant flux of the emitter. Moreover, waveguides outperformed LEDs regarding spectral selectivity. oCIs with either emitter type showed greater spectral selectivity when compared to eCI. In addition, modeling the effects of the source-to-SGN distance, orientation of the sources and impact of scar tissue further informs the development of optogenetic hearing restoration

High Fidelity Bioelectric Modelling of the Implanted Cochlea

Cochlear implants are medical devices that can restore sound perception in individuals with sensorineural hearing loss (SHL). Since their inception, improvements in performance have largely been driven by advances in signal processing, but progress has plateaued for almost a decade. This suggests that there is a bottleneck at the electrode-tissue interface, which is responsible for enacting the biophysical changes that govern neuronal recruitment. Understanding this interface is difficult because the cochlea is small, intricate, and difficult to access. As such, researchers have turned to modelling techniques to provide new insights. The state-of-the-art involves calculating the electric field using a volume conduction model of the implanted cochlea and coupling it with a neural excitation model to predict the response. However, many models are unable to predict patient outcomes consistently. This thesis aims to improve the reliability of these models by creating high fidelity reconstructions of the inner ear and critically assessing the validity of the underlying and hitherto untested assumptions. Regarding boundary conditions, the evidence suggests that the unmodelled monopolar return path should be accounted for, perhaps by applying a voltage offset at a boundary surface. Regarding vasculature, the models show that large modiolar vessels like the vein of the scala tympani have a strong local effect near the stimulating electrode. Finally, it appears that the oft-cited quasi-static assumption is not valid due to the high permittivity of neural tissue. It is hoped that the study improves the trustworthiness of all bioelectric models of the cochlea, either by validating the claims of existing models, or by prompting improvements in future work. Developing our understanding of the underlying physics will pave the way for advancing future electrode array designs as well as patient-specific simulations, ultimately improving the quality of life for those with SHL

Computational processing and analysis of ear images

Tese de mestrado. Engenharia Biomédica. Faculdade de Engenharia. Universidade do Porto. 201

Brain Tumour Classification Using Deep Features from Structural MRI

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Three-dimensional models of cochlear implants : a review of their development and how they could support management and maintenance of cochlear implant performance

Three-dimensional (3D) computational modelling of the auditory periphery forms an integral part of modern-day research in cochlear implants (CIs). These models consist of a volume conduction description of implanted stimulation electrodes and the current distribution around these, coupled to auditory nerve fibre models. Cochlear neural activation patterns can then be predicted for a given input stimulus. The objective of this article is to present the context of 3D modelling within the field of CIs, the different models and approaches to models that have been developed over the years, as well as the applications and potential applications of these models. The process of development of 3D models is discussed, and the article places specific emphasis on the complementary roles of generic models and user-specific models, as the latter is important for translation of these models into clinical application.http://tandfonline.com/toc/inet202017-05-31hb2016Electrical, Electronic and Computer Engineerin