Cochlear implantation modeling and functional evaluation considering uncertainty and parameter variability

Recent innovations in computational modeling have led to important advances towards the development of predictive tools to simulate and optimize surgery outcomes. This thesis is focused on cochlear implantation surgery, technique which allows recovering functional hearing in patients with severe deafness. The success of this intervention, however, relies on factors, which are unpredictable or difficult to control. This, combined with the high variability of hearing restoration levels among patients, makes the prediction of this surgery a very challenging process. The aim of this thesis is to develop computational tools to assess the functional outcome of the cochlear implantation. To this end, this thesis addresses a set of challenges, such as the automatic optimization of the implantation and stimulation parameters by evaluating the neural response evoked by the cochlear implant or the functional evaluation of a large set of virtual patients.Recientes mejoras en el desarrollo del modelado computacional han facilitado importantes avances en herramientas predictivas para simular procesos quirúrgicos maximizando así los resultados de la cirugía. Esta tesis se focaliza en la cirugía de implantación coclear. Dicha técnica permite recuperar el sentido auditivo a pacientes con sordera severa. Sin embargo, el éxito de la intervención depende de un conjunto de factores, difíciles de controlar o incluso impredecibles. Por este motivo, existe una gran variabilidad interindividual, lo cual lleva a considerar la predicción de esta cirugía como un proceso complejo. El objetivo de esta tesis es el desarrollo de herramientas computacionales para la evaluación funcional de dicha cirugía. Para este fi n, esta tesis aborda una serie de retos, entre ellos la optimización automática de la respuesta neural inducida por el implante coclear y la evaluación numérica de grandes grupos de pacientes

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.This work is partly supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) and by the European Union Seventh Frame Programme (FP7/2007-2013), Grant agreement 304857, HEAR-EU project

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.This work was financially supported by the European Commission (FP7 project number 304857, HEAR-EU), Generalitat de Catalunya (PRODUCTE program, project number 2016PROD00047) and the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502)

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.This work was partly supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Program (MDM-2015-0502), by the AGAUR grant 2016-PROD-00047, the European Union Seventh Framework Program (FP7/2007-2013), Grant agreement 304857, HEAR-EU project and the QUAES Foundation Chair for Computational Technologies for Healthcare

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.This work was financially supported by the European Commission FP7 (HEAR-EU European project #304857 http://www.hear-eu.eu) and the Swiss National Science Foundation (Nano- Tera initiative project title Hear-Restore). In addition, this work is partly supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502)