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Modeling Electrical Conduction in the Cochlea for Implant Engineering

Modeling Electrical Conduction in the Cochlea for Implant Engineering
植入工程中耳蜗的电传导建模
批准号:
RGPIN-2022-04184
负责人:
Ladak, Hanif
金额:
$4.01万
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2022
资助国家:
加拿大
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31

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中文摘要
翻译
人工耳蜗(CI)是一种复杂的电子设备,用于为聋人提供声音感觉。听力结果是高度可变的,部分原因可能是植入物接受者之间的解剖差异。没有测量CI产生的详细电压模式的方法;但是,该信息将有助于了解个体解剖结构如何影响性能。在这里,我们将使用计算机模拟来计算电压,目的是了解患者特定CI性能的机制。 我的长期目标是使用工程方法,如成像和模拟,以提高CI的性能。这项建议的具体目标是:(1)根据图像开发解剖学上精确的计算机模型,以计算由CI产生的电压,并量化它们如何基于个体解剖学而变化,(2)开发数学方法以估计组织电特性,(3)开发软件以从低分辨率诊断图像自动生成高分辨率模型,以促进患者耳朵的建模,(4)发展和应用数学方法优化CI设计。科学方法:我和我的团队收集了尸体耳朵和头部的独特图像,使用同步加速器,一种线性加速器和X射线来描绘建模中感兴趣的所有结构。我们将使用这些图像定义计算机模型的详细几何形状,并使用功能强大的有限元方法模拟电压模式(目标1)。这些模型将用于研究如何使用简化的几何形状,如在过去的建模研究影响计算的电压。对于目标2,将开发一种数学优化方法,以找到每个耳朵的“最佳”电特性值。最终,我们希望创建一种易于使用的患者特定建模技术,以优化每个患者的CI设置。同步加速器不能用于患者,因此我们将开发计算机视觉软件作为Aim 3的一部分,以从较低分辨率的诊断扫描仪生成高保真模型。我们的同步加速器图像将被用来训练所提出的算法。最后,在目标4中,我们将开发数学方法和软件来优化CI设计。影响:本研究的新颖技术贡献包括(1)生成高保真计算机模型的方法,沿着误差估计,(2)电特性估计算法,(3)将我们的建模方法纳入医院日常使用的软件,以及(4)实现最佳CI设计和设置的方法。在这项工作中开发的工具将改善听力结果,并支持CI制造商,其中许多在加拿大有业务。参与该项目的学员将学习先进的成像和计算机模拟工具,这将使他们在几个行业中具有很高的就业能力,而不仅仅是听力植入行业。
英文摘要
CONTEXT: A cochlear implant (CI) is a complex electronic device used to provide the sensation of sound to deaf individuals. Hearing outcomes are highly variable and may arise in part due to anatomical differences amongst implant recipients. There are no methods to measure detailed voltage patterns produced by CIs; however, this information would help in understanding how individual anatomy affects performance. Here, we will use computer simulation to compute voltages with the aim of understanding the mechanisms underlying patient-specific CI performance. OBJECTIVES: My long-term objective is to use engineering methods such as imaging and simulation to improve the performance of CIs. The specific aims of this proposal are to: (1) develop anatomically accurate computer models from images to calculate electrical voltages generated by a CI and quantify how they vary based on individual anatomy, (2) develop mathematical methods to estimate tissue electrical properties, (3) develop software to automatically generate high-resolution models from low-resolution diagnostic images to facilitate modeling of patients' ears, and (4) develop and apply mathematical methods to optimize CI design. SCIENTIFIC APPROACH: My team and I have collected unique images of cadaveric ears and heads using a synchrotron, a type of linear accelerator, and X-rays to depict all structures of interest in modeling. We will define the detailed geometry of computer models using these images and simulate voltage patterns using the powerful finite-element method (Aim 1). These models will be used to investigate how using simplified geometry as in past modeling studies affects computed voltages. For Aim 2, a mathematical optimization method will be developed to find the "best" electrical property values for each individual ear. Ultimately, we would like to create an easy to use, patient-specific modeling technique to optimize CI settings on a per-patient basis. Synchrotrons cannot be used on patients, so we will develop computer vision software as part of Aim 3 to generate high-fidelity models from lower resolution diagnostic scanners. Our synchrotron images will be used to train the proposed algorithms. Lastly, in Aim 4, we will develop mathematical methods and software to optimize CI design. IMPACT: Novel technical contributions of this research include (1) methods to generate high-fidelity computer models along with error estimates, (2) algorithms for electrical property estimation, (3) software to move our modeling methodologies into routine use in hospitals, and (4) methods to achieve optimal CI designs and settings. The tools developed in this work will improve hearing outcomes and support CI manufacturers, many of which have operations in Canada. Trainees working on this project will learn advanced imaging and computer simulation tools that will make them highly employable in several industries, not just the hearing implant industry.
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Mechanics of the Middle Ear
  • 批准号:
    RGPIN-2015-03799
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.6万
  • 财政年份:
    2021
  • 负责人:
    Ladak, Hanif
  • 依托单位:
Mechanics of the Middle Ear
  • 批准号:
    RGPIN-2015-03799
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.6万
  • 财政年份:
    2020
  • 负责人:
    Ladak, Hanif
  • 依托单位:
Mastoidectomy Simulator for Surgical Training and Rehearsal
  • 批准号:
    508411-2017
  • 项目类别:
    Collaborative Health Research Projects
  • 资助金额:
    $7.08万
  • 财政年份:
    2018
  • 负责人:
    Ladak, Hanif
  • 依托单位:
Mechanics of the Middle Ear
  • 批准号:
    RGPIN-2015-03799
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.6万
  • 财政年份:
    2018
  • 负责人:
    Ladak, Hanif
  • 依托单位:
海外基金