Image-Guided Cochlear Implant Programming Techniques

图像引导人工耳蜗植入编程技术

• 批准号: 9060285
• 负责人: Jack Noble
• 金额: $ 37.42万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9060285
• 关键词: Accounting; Address; Adult; Affect; Audiology; Auditory; Characteristics; Clinical; Cochlea; Cochlear Implants; Cues; Development; Devices; Effectiveness; Electrodes; Estimation Techniques; Fiber; Frequencies; Goals; Health; Hearing; Hour; Image; Image Analysis; Implanted Electrodes; Individual; Lead; Maps; Measures; Methods; Modeling; Morphologic artifacts; Nerve; Neural Pathways; Noise; Operative Surgical Procedures; Outcome; Patient Selection; Patients; Pattern; Performance; Physics; Positioning Attribute; Process; Property; Publications; Research; Resolution; Sensory; Signal Transduction; Site; Specific qualifier value; Speech; System; Techniques; Technology; Testing; Time; Work; X-Ray Computed Tomography; base; design; experience; hearing impairment; heuristics; image guided; image processing; implantation; improved; neural stimulation; programs; relating to nervous system; research study; restoration; sound; sound frequency; standard of care

DESCRIPTION (provided by applicant): The goals of this research are to develop and evaluate new patient-customized, Image-Guided Cochlear Implant Programming (IGCIP) strategies. With over 320,000 recipients worldwide, cochlear implants (CIs) are considered standard of care treatment for severe-to-profound sensory-based hearing loss. The programming process that audiologists use clinically is one factor that limits outcomes because, while existing devices permit manipulation of very many settings that could lead to better performance, there are no objective cues available to indicate what setting changes will lead to better performance. Any advancement that accelerates convergence to settings that better approximate natural fidelity could have significant impact for CI recipients, clinicians, and audiology centers. The goal of this project is to develop and evaluate new IGCIP strategies that could provide object information to the programming process and lead to programs that better approximate natural fidelity. In natural hearing, each neural fiber (out of ~30,000) is activated when its characteristic frequency (CF) is present in a sound. With a CI, due to the small number of electrodes (12 to 22), their large size relative to the individual nerves, and their wide curren spread, limited spectral resolution has been achievable, thus each electrode stimulates nerves corresponding to a wide range of CFs. Since this is generally not accounted for in traditional programming, sub-optimal settings are typically chosen that result in interacting channels, causing spectral smearing artifacts. Further, since the stimulation patterns of the electrodes are unknown, the CFs stimulated by each electrode are unknown. Thus the sound frequencies assigned to each electrode do not generally correspond to the CFs of the nerves it stimulates, resulting in frequency mismatch artifacts. These limitations negatively affect outcomes and, while known, have been difficult to address. The hypothesis of this study is that more objective, important information can be obtained through analysis of patient CT images and can be used to customize CI settings for improved hearing performance. The IGCIP strategies that will be tested will involve using imaging to detect where spectral smearing and frequency mismatching is occurring and to minimize these artifacts through selection of patient-customized program settings, including frequency table settings, current steering settings, and current focusing settings. To support the design of IGCIP strategies, an approach for using patient CT images to create patient-specific, comprehensive models of electrical current flow and the CI's neural activation patterns will also be developed. Since IGCIP strategies require only simple changes of CI settings, they work with existing device technology, do not require further surgery, and are reversible. If successful, a suite of IGCIP techniques that can objectively guide the programming of CIs towards optimized settings and improve hearing restoration for new and existing CI recipients will be developed in this project.

描述（由申请人提供）：这项研究的目标是开发和评估新的患者注定，图像引导的人工耳蜗编程（IGCIP）策略。 在全球范围内拥有超过32万名接收者，人工耳蜗（CIS）被认为是严重基于感觉的听力损失的护理标准。 听力学家在临床上使用的编程过程是限制结果的一个因素，因为尽管现有设备允许操纵许多可能导致更好性能的设置，但没有客观的线索可用于表明哪些设置更改会导致更好的性能。 任何加速融合到更好地近似自然保真度的环境的进步都可能对CI接受者，临床医生和听力学中心产生重大影响。 该项目的目的是制定和评估新的IGCIP策略，这些策略可以为编程过程提供对象信息，并导致更好地近似自然保真度的程序。 在自然听力中，当声音中存在每个神经纤维（在约30,000个中），其特征频率（CF）被激活。 由于CI，由于电极数量少（12至22），其大小相对于单个神经，并且其宽阔的Curren扩散，因此可以实现有限的光谱分辨率，因此每个电极都会刺激与广泛CFS相对应的神经。 由于通常在传统编程中不考虑这，因此通常会选择次优的设置，从而导致相互作用的通道，从而导致光谱涂抹伪影。 此外，由于电极的刺激模式是 未知，每个电极刺激的CF尚不清楚。 因此，分配给每个电极的声音频率通常与它刺激的神经的CFS相对应，从而导致频率不匹配伪像。 这些局限性对结果产生负面影响，虽然已知，但很难解决。 这项研究的假设是，可以通过分析患者CT图像来获得更客观的重要信息，并可用于自定义CI设置以改善听力表现。 将要测试的IGCIP策略将涉及使用成像来检测光谱涂抹和频率不匹配的发生，并通过选择患者注定的程序设置，包括频率表设置，当前转向设置和当前的聚焦设置，以最大程度地减少这些工件。 为了支持IGCIP策略的设计，还将开发一种使用患者CT图像创建患者特异性电流流量模型和CI神经激活模式的方法。 由于IGCIP策略仅需要简单的CI设置更改，因此它们可以与现有的设备技术一起使用，不需要进一步的手术，并且是可逆的。 如果成功的话，将在该项目中开发一套IGCIP技术，可以客观地指导CIS针对优化设置进行编程，并改善新的和现有CI接收者的听力修复。