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.

Patient-specific estimation of detailed cochlear shape from clinical CT images.

PURPOSE: A personalized estimation of the cochlear shape can be used to create computational anatomical models to aid cochlear implant (CI) surgery and CI audio processor programming ultimately resulting in improved hearing restoration. The purpose of this work is to develop and test a method for estimation of the detailed patient-specific cochlear shape from CT images. METHODS: From a collection of temporal bone [Formula: see text]CT images, we build a cochlear statistical deformation model (SDM), which is a description of how a human cochlea deforms to represent the observed anatomical variability. The model is used for regularization of a non-rigid image registration procedure between a patient CT scan and a [Formula: see text]CT image, allowing us to estimate the detailed patient-specific cochlear shape. RESULTS: We test the accuracy and precision of the predicted cochlear shape using both [Formula: see text]CT and CT images. The evaluation is based on classic generic metrics, where we achieve competitive accuracy with the state-of-the-art methods for the task. Additionally, we expand the evaluation with a few anatomically specific scores. CONCLUSIONS: The paper presents the process of building and using the SDM of the cochlea. Compared to current best practice, we demonstrate competitive performance and some useful properties of our method.

Towards a Complete In Silico Assessment of the Outcome of Cochlear Implantation Surgery.

Cochlear implantation (CI) surgery is a very successful technique, performed on more than 300,000 people worldwide. However, since the challenge resides in obtaining an accurate surgical planning, computational models are considered to provide such accurate tools. They allow us to plan and simulate beforehand surgical procedures in order to maximally optimize surgery outcomes, and consequently provide valuable information to guide pre-operative decisions. The aim of this work is to develop and validate computational tools to completely assess the patient-specific functional outcome of the CI surgery. A complete automatic framework was developed to create and assess computationally CI models, focusing on the neural response of the auditory nerve fibers (ANF) induced by the electrical stimulation of the implant. The framework was applied to evaluate the effects of ANF degeneration and electrode intra-cochlear position on nerve activation. Results indicate that the intra-cochlear positioning of the electrode has a strong effect on the global performance of the CI. Lateral insertion provides better neural responses in case of peripheral process degeneration, and it is recommended, together with optimized intensity levels, in order to preserve the internal structures. Overall, the developed automatic framework provides an insight into the global performance of the implant in a patient-specific way. This enables to further optimize the functional performance and helps to select the best CI configuration and treatment strategy for a given patient.

A multiscale imaging and modelling dataset of the human inner ear.

Understanding the human inner ear anatomy and its internal structures is paramount to advance hearing implant technology. While the emergence of imaging devices allowed researchers to improve understanding of intracochlear structures, the difficulties to collect appropriate data has resulted in studies conducted with few samples. To assist the cochlear research community, a large collection of human temporal bone images is being made available. This data descriptor, therefore, describes a rich set of image volumes acquired using cone beam computed tomography and micro-CT modalities, accompanied by manual delineations of the cochlea and sub-compartments, a statistical shape model encoding its anatomical variability, and data for electrode insertion and electrical simulations. This data makes an important asset for future studies in need of high-resolution data and related statistical data objects of the cochlea used to leverage scientific hypotheses. It is of relevance to anatomists, audiologists, computer scientists in the different domains of image analysis, computer simulations, imaging formation, and for biomedical engineers designing new strategies for cochlear implantations, electrode design, and others.

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.

Automatic Model Generation Framework for Computational Simulation of Cochlear Implantation.

Recent developments in computational modeling of cochlear implantation are promising to study in silico the performance of the implant before surgery. However, creating a complete computational model of the patient's anatomy while including an external device geometry remains challenging. To address such a challenge, we propose an automatic framework for the generation of patient-specific meshes for finite element modeling of the implanted cochlea. First, a statistical shape model is constructed from high-resolution anatomical µCT images. Then, by fitting the statistical model to a patient's CT image, an accurate model of the patient-specific cochlea anatomy is obtained. An algorithm based on the parallel transport frame is employed to perform the virtual insertion of the cochlear implant. Our automatic framework also incorporates the surrounding bone and nerve fibers and assigns constitutive parameters to all components of the finite element model. This model can then be used to study in silico the effects of the electrical stimulation of the cochlear implant. Results are shown on a total of 25 models of patients. In all cases, a final mesh suitable for finite element simulations was obtained, in an average time of 94 s. The framework has proven to be fast and robust, and is promising for a detailed prognosis of the cochlear implantation surgery.

Computational Models for Predicting Outcomes of Neuroprosthesis Implantation: the Case of Cochlear Implants.

Electrical stimulation of the brain has resulted in the most successful neuroprosthetic techniques to date: deep brain stimulation (DBS) and cochlear implants (CI). In both cases, there is a lack of pre-operative measures to predict the outcomes after implantation. We argue that highly detailed computational models that are specifically tailored for a patient can provide useful information to improve the precision of the nervous system electrode interface. We apply our framework to the case of CI, showing how we can predict nerve response for patients with both intact and degenerated nerve fibers. Then, using the predicted response, we calculate a metric for the usefulness of the stimulation protocol and use this information to rerun the simulations with better parameters.