Image-Guided Cochlear Implant Programming Techniques

图像引导人工耳蜗植入编程技术

• 批准号: 8752841
• 负责人: Jack Noble
• 金额: $ 38.82万
• 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/8752841
• 关键词: Accounting; Address; Adult; Affect; Audiology; Auditory; Characteristics; Clinical; Cochlea; Cochlear Implants; Cues; Development; Devices; Effectiveness; Electrodes; Estimation Techniques; Fiber; Frequencies; Goals; 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; Relative (related person); 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 processing; implantation; improved; neural stimulation; programs; public health relevance; 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）：本研究的目标是开发和评估新的患者定制、图像引导人工耳蜗编程 (IGCIP) 策略。 全球有超过 320,000 名接受者，人工耳蜗 (CI) 被认为是治疗重度至极重度感觉性听力损失的标准护理治疗。 听力学家在临床上使用的编程过程是限制结果的因素之一，因为虽然现有设备允许操纵许多可以带来更好表现的设置，但没有客观的线索来表明哪些设置变化会带来更好的表现。 任何加速融合到更接近自然保真度的设置的进步都可能对 CI 接受者、临床医生和听力学中心产生重大影响。 该项目的目标是开发和评估新的 IGCIP 策略，该策略可以为编程过程提供对象信息，并生成更接近自然保真度的程序。 在自然听力中，每根神经纤维（约 30,000 根）在声音中出现其特征频率 (CF) 时都会被激活。 对于 CI，由于电极数量较少（12 至 22 个）、相对于单个神经的尺寸较大以及电流分布较宽，因此可以实现有限的光谱分辨率，因此每个电极刺激对应于较宽范围的神经。 CF。 由于传统编程中通常没有考虑到这一点，因此通常会选择次优设置，从而导致通道交互，从而导致光谱模糊伪影。 此外，由于电极的刺激模式是 未知，每个电极刺激的 CF 未知。 因此，分配给每个电极的声音频率通常不对应于它刺激的神经的 CF，从而导致频率不匹配伪影。 这些限制会对结果产生负面影响，虽然众所周知，但很难解决。 本研究的假设是，通过分析患者 CT 图像可以获得更客观、更重要的信息，并可用于定制 CI 设置以改善听力表现。 将测试的 IGCIP 策略将涉及使用成像来检测发生光谱拖尾和频率不匹配的位置，并通过选择患者定制的程序设置（包括频率表设置、当前转向设置和当前聚焦设置）来最大限度地减少这些伪影。 为了支持 IGCIP 策略的设计，还将开发一种使用患者 CT 图像创建患者特定的综合电流模型和 CI 神经激活模式的方法。 由于 IGCIP 策略仅需要简单更改 CI 设置，因此它们可与现有设备技术配合使用，不需要进一步手术，并且是可逆的。 如果成功，该项目将开发一套 IGCIP 技术，可以客观地指导 CI 的编程以实现优化设置，并改善新的和现有的 CI 接受者的听力恢复。