Image-based frequency reallocation for optimizing cochlear implant programming

基于图像的频率重新分配，用于优化人工耳蜗编程

• 批准号: 8356935
• 负责人: Jack Noble
• 金额: $ 19.1万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8356935
• 关键词: Acoustic Nerve; Algorithms; Anatomy; Apical; Basilar Membrane; Characteristics; Clinical; Cochlea; Cochlear Implants; Computer Assisted; Computer software; Detection; Ear; Effectiveness; Electric Stimulation; Electrodes; Equation; Frequencies; Future; Goals; Hearing; Image; Implant; Implanted Electrodes; Individual; Knowledge; Lead; Left; Location; Manufacturer Name; Maps; Measures; Methods; Modeling; Morphologic artifacts; Nerve; Outcome; Patients; Performance; Positioning Attribute; Postoperative Period; Process; Relative (related person); Research; Research Personnel; Resolution; Scheme; Sensory; Shapes; Signal Transduction; Site; Stimulus; Structure; Techniques; Technology; Testing; Time; Translating; Variant; Work; base; electric field; falls; hearing impairment; heuristics; image processing; improved; in vivo; neural stimulation; programs; relating to nervous system; response; restoration; software development; sound; sound frequency; spiral ganglion; standard of care; vibration

DESCRIPTION (provided by applicant): The goals of this research are to develop and assess the clinical utility of an approach for determining the position of implanted cochlear implant (CI) electrodes relative to stimulation targets (the nerves of the Spiral Ganglion (SG)) for CI tuning assistance. It is widely believed that the best hearing restoration outcome can be achieved by stimulating, for a particular sound, the nerves that naturally correspond to the spectrum of that sound. However, this is not currently possible due to several technical limitations. One such issue is that the positions of the implanted electrodes are unknown. Thus, the audiologist adjusts the signal characteristics assigned to each electrode based solely on patient response. The majority of potentially adjustable parameters are left at the default settings determined by the CI manufacturer. Because of this one-size-fits-all approach, the tuning process may not result in optimal hearing restoration for all recipients. Each electrode is positioned at variable depths and perimodiolar distances. Electrode depth discrepancies result in a frequency shift artifact, i.e., each electrode stimulates nerves that do not correspond to the frequencies of the detected sound. A larger distance to the SG leads to wider current spread from each electrode, decreasing the spectral resolution, i.e., each electrode stimulates many nerves corresponding to a wide range of frequencies. In future work, we would like to test a range of advanced tuning techniques that rely on knowing the position of implanted electrodes relative to tonotopically mapped SG nerves. These techniques have the potential to improve hearing outcomes achieved with existing CIs. However, there has been no technology developed that allows accurate assessment of electrode position relative to stimulation targets in vivo. In this research, we will develop this technology and test a simple tuning scheme to assess its clinical utility. If successful, we will have developed an easy to use software package that has the potential to increase the effectiveness of existing cochlear implant technology and improve the quality of hearing restoration for implantees. To identify electrode position, existing techniques for identifying Cochlear Contour Advance electrode arrays in post-operative CT will be expanded for application to other types of electrodes. To identify stimulation targets (the SG), advanced active shape modeling techniques will be explored. These are powerful image processing methods that identify structures in images while constraining the shape of the results to be consistent with typical anatomical variations. The SG region will be tonotopically mapped using known heuristic equations that describe this frequency relationship. A simple tuning scheme will then be tested on implantees to evaluate the utility of this approach. Tuning parameters will include deciding which electrodes are on and off, determining the relative power of each electrode, and determining an electrode frequency allocation table. Positive results will demonstrate that these techniques will lead to more effective implant tuning and better hearing restoration. PUBLIC HEALTH RELEVANCE: The proposed project involves exploring algorithmic methods that will lead to performance optimizations of cochlear implant electrode arrays. Thus, the results of this project can potentially improve the quality of hearing restoration for cochlear implant users and improve efficiency of the tuning process.

描述（由申请人提供）：这项研究的目标是开发和评估确定植入的人工耳蜗（CI）位置的方法的临床实用性（CI） 电极相对于刺激靶标（螺旋神经节神经（SG））用于CI调节辅助。 人们普遍认为，可以通过刺激自然对应于该声音的频谱的神经来实现最佳的听力恢复结果。 但是，由于几个技术限制，目前不可能。 一个问题是植入电极的位置尚不清楚。 因此，听力学家仅根据患者反应来调整分配给每个电极的信号特性。 大多数可能可调节的参数留在CI制造商确定的默认设置处。 由于这种千篇一律的方法，调整过程可能不会为所有接受者带来最佳的听力恢复。 每个电极都位于可变 深度和外段距离。 电极深度差异导致频移伪影，即，每个电极刺激与检测到的声音频率相对应的神经。 到达SG的距离更大会导致从每个电极散布更广泛的电流，从而降低光谱分辨率，即，每个电极刺激了许多与广泛频率相对应的神经。 在将来的工作中，我们希望测试一系列高级调整技术，这些技术依赖于了解植入电极相对于打电话映射的SG神经的位置。 这些技术有可能改善现有独联体实现的听力结果。 但是，没有开发出相对于体内刺激靶标的电极位置进行准确评估的技术。 在这项研究中，我们将开发这项技术，并测试一个简单的调整方案，以评估其临床实用性。 如果成功的话，我们将开发一个易于使用的软件包，该软件包有可能提高现有的人工耳蜗技术的有效性并提高植入物的听力修复质量。 要识别电极位置，现有技术 为了识别术后CT中的耳蜗轮廓前电极阵列，将扩展以应用于其他类型的电极。 为了识别刺激目标（SG），将探索先进的主动形状​​建模技术。 这些是功能强大的图像处理方法，可以识别图像中的结构，同时约束结果的形状，以使其与典型的解剖变化一致。 SG区域将使用描述这种频率关系的已知启发式方程来进行体调映射。 然后，将对植入物进行一个简单的调整方案，以评估这种方法的实用性。 调整参数将包括确定哪些电极打开和关闭，确定每个电极的相对功率，并确定电极频率分配表。 积极的结果将表明，这些技术将导致更有效的植入物调整和更好的听力恢复。 公共卫生相关性：拟议的项目涉及探索算法方法，这将导致人工耳蜗电极阵列的性能优化。 因此，该项目的结果可能会提高人工耳蜗使用者的听力修复质量并提高调整过程的效率。