Broad Bandwidth Transducers for High Resolution Information Rich IVUS

宽带宽传感器可提供高分辨率信息丰富的 IVUS

• 批准号: 10642851
• 负责人: AARON J FLEISCHMAN
• 金额: $ 37.03万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-10 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10642851
• 关键词: Address; Algorithms; Animals; Area; Arterial Fatty Streak; Biological Markers; Blood Vessels; Cadaver; Cardiovascular system; Carotid Arteries; Carotid Artery Plaques; Catheters; Classification; Clinical; Clinical Research; Computer software; Coronary; Coronary Arteriosclerosis; Custom; Decision Making; Devices; Diagnosis; Disadvantaged; Dissection; Ensure; Evaluation; Event; Flushing; Focused Ultrasound; Frequencies; Friends; Future; Geometry; Histology; Image; Intervention; Kidney Diseases; Length; Machine Learning; Magnetic Resonance Imaging; Measurement; Measures; Methods; Morphology; Noise; Optical Coherence Tomography; Oryctolagus cuniculus; Output; Patients; Penetration; Performance; Pharmacotherapy; Physicians; Polychlorinated Biphenyls; Polymers; Predictive Factor; Prevalence; Procedures; Production; Reader; Research; Research Personnel; Resolution; Risk Factors; Role; Rotation; Signal Transduction; Stents; Structure; System; Technology; Testing; Thick; Thinness; Thrombosis; Tissues; Transducers; Tumor Debulking; Ultrasonic Transducer; Ultrasonics; Ultrasonography; Visualization; Work; commercialization; coronary event; cost; deep learning; design; harmonic distortion; image visualization; imaging biomarker; imaging modality; imaging system; improved; innovation; integrated circuit; interest; machine learning algorithm; machine learning method; manufacture; microelectronics; novel; printed circuit board; software development; success; tool; treatment planning; ultrasound; virtual; volcano

Abstract Intra-coronary imaging is a powerful clinical tool for decision making, treatment planning, and assessment of stent deployment. It is also a powerful research tool for plaque progression/regression, drug treatments, and device interventions. There are clear advantages and disadvantages of common intravascular imaging methods. Intravascular ultrasound (IVUS) provides good resolution and allows one to measure lumen narrowing, wall thickening, atheroma burden, and to a lesser extent stent deployment. Using spectral analysis of the RF signal and machine learning, our group has developed software, which was later commercialized, to automatically classify atherosclerotic tissues using IVUS images. Intravascular optical coherence tomography (IVOCT) has better resolution than IVUS, enabling visualization and analysis of stent struts, thin caps of vulnerable plaques, thrombosis, and plaque erosion. IVUS has better tissue penetration than IVOCT, enabling one to assess total plaque burden. In addition, IVUS, unlike IVOCT, does not require one to flush the blood from the vessel prior to imaging, a significant issue for patients, given the prevalence of kidney disease. These limitations suggest an unmet need for a new intravascular imaging modality with attributes of both IVUS and IVOCT. We will create a novel intravascular, high frequency, broadband, focused ultrasound system (H-IVUS), which will address clinical needs identified for IVOCT and conventional IVUS. H-IVUS will have near-IVOCT resolution to enable identification of critical small structures (e.g., thin caps and stent struts), while maintaining the ability of ultrasound to penetrate tissue and evaluate soft plaque burden. It will have immediate clinical impact by ena- bling clinicians to plan and optimize procedures that have already shown to benefit from intravascular imaging: determine true vessel size, identify stent landing zones to choose correct stent lengths, identify plaque morphol- ogies to guide debulking, detect edge dissection, determine stent malapposition, and detect thin caps. In addi- tion, the high bandwidth of H-IVUS provides both fundamental and harmonic bands, which are expected to im- prove tissue classification, as determined by us in carotid arteries. We will use broadband wavelet analysis of RF, spatial structures in images, and machine learning to determine if wideband H-IVUS can provide improved segmentation to improve recognition of the important clinical landmarks and provide superior automated plaque classification over current VH IVUS®, which uses only narrow RF-fundamental-band stationary spectral analysis. In addition, our manufacturing-friendly design should greatly reduce cost, thereby limiting this barrier to utiliza- tion. Specifically, we will develop a catheter-based H-IVUS PCB using a focused polymeric ultrasonic transducer; develop algorithms which utilize broadband RF and harmonic imaging to analyze tissue types w and rigorously compare results to conventional IVUS and IVOCT. Our research will provide numerous innovations and enable creation of a product to disrupt intracoronary imaging.

抽象的 冠状动脉内成像是一种强大的临床工具，可用于决策、治疗计划和评估 支架部署。它也是斑块进展/消退、药物治疗和药物治疗的强大研究工具。 设备干预。常见的血管内成像方法有明显的优点和缺点。 血管内超声 (IVUS) 提供良好的分辨率，可测量管腔变窄、管壁 增厚、粥样斑块负担以及较小程度的支架展开。使用射频信号的频谱分析 和机器学习，我们组开发了软件，后来商业化了，可以自动 使用 IVUS 图像对动脉粥样硬化组织进行分类。血管内光学相干断层扫描（IVOCT） 比 IVUS 具有更好的分辨率，能够对支架支柱、易损斑块的薄帽进行可视化和分析， 血栓形成和斑块侵蚀。 IVUS 比 IVOCT 具有更好的组织穿透性，使人们能够评估总体 斑块负担。此外，与 IVOCT 不同，IVUS 不需要在检查前冲洗血管中的血液。 鉴于肾脏疾病的流行，影像学检查对于患者来说是一个重要问题。这些限制表明 对具有 IVUS 和 IVOCT 属性的新型血管内成像模式的需求尚未得到满足。 我们将创建一种新型血管内、高频、宽带、聚焦超声系统（H-IVUS）， 将满足 IVOCT 和传统 IVUS 确定的临床需求。 H-IVUS 将具有接近 IVOCT 的分辨率 能够识别关键的小型结构（例如薄盖和支架支柱），同时保持能力 超声波穿透组织并评估软斑块负荷。它将通过ena-产生直接的临床影响 使临床医生能够规划和优化已被证明可受益于血管内成像的手术： 确定真实的血管尺寸，识别支架着陆区域以选择正确的支架长度，识别斑块吗啡- 指导减灭术、检测边缘剥离、确定支架贴壁不良以及检测薄帽。另外， 化，H-IVUS 的高带宽提供了基波和谐波频带，预计这些频带将增强 证明我们在颈动脉中确定的组织分类。我们将使用宽带小波分析 RF、图像中的空间结构和机器学习，以确定宽带 H-IVUS 是否可以提供改进的 分割以提高对重要临床标志的识别并提供卓越的自动化斑块 与当前 VH IVUS® 相比的分类，后者仅使用窄射频基带静态频谱分析。 此外，我们的制造友好型设计应大大降低成本，从而限制利用这一障碍 。具体来说，我们将使用聚焦聚合物超声波换能器开发基于导管的 H-IVUS PCB； 开发利用宽带射频和谐波成像的算法来严格分析组织类型 将结果与传统 IVUS 和 IVOCT 进行比较。我们的研究将提供大量创新并使 开发一种破坏冠状动脉内成像的产品。