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: 10447462
• 负责人: AARON J FLEISCHMAN
• 金额: $ 39.03万
• 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/10447462
• 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; Future; Geometry; Gold; Histology; Image; Intervention; Kidney Diseases; Lead; Length; Machine Learning; Magnetic Resonance Imaging; Measurement; Measures; Methods; 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; Signal Transduction; Stents; Structure; System; Technology; Testing; Thick; Thinness; Thrombosis; Time; Tissues; Transducers; Tumor Debulking; Ultrasonic Transducer; Ultrasonics; Ultrasonography; Visualization; Width; Work; base; coronary event; cost; deep learning; design; harmonic distortion; image visualization; imaging biomarker; imaging modality; improved; innovation; integrated circuit; interest; machine learning algorithm; machine learning method; 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 冠状成像是用于决策，治疗计划和评估的强大临床工具 支架部署。它也是一种强大的研究工具，用于牙菌斑的进展/回归，药物治疗和 设备干预。常见的血管内成像方法有明显的优势和缺点。 血管内超声（IVUS）提供良好的分辨率，并允许一个人测量腔狭窄，墙壁 增厚，动脉粥样硬化伯恩（Atheroma Burnen）以及支架部署的程度较小。使用RF信号的光谱分析 和机器学习，我们的小组开发了软件，后来已商业化，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）具有 比IVU的分辨率更好，可以实现支架支撑杆的可视化和分析，易受伤害的斑块的薄帽， 血栓形成和斑块侵蚀。 IVU的组织穿透性比IVOCT更好，使一个人能够评估总 牌匾负担。此外，IVU与IVOCT不同，不需要一个在 鉴于肾脏疾病的患病率，成像是患者的重要问题。这些限制表明 未满足新的血管内成像方式，具有IVU和IVOCT的属性。 我们将创建一种新型的血管内，高频，宽带，聚焦超声系统（H-IVUS），该系统，该系统，该系统 将解决针对IVOCT和常规IVU的临床需求。 H-IVU将具有接近IVOCT的分辨率 能够鉴定关键的小结构（例如薄盖和支架支柱），同时保持能力 超声渗透组织并评估软牌匾负担。它将立即受到ENA的临床影响 Bling临床医生计划和优化已经显示出从血管内成像中受益的程序： 确定真正的容器尺寸，确定支架着陆区以选择正确的支架长度，识别斑块吗啡 - 指导脱孔，检测边缘解剖，确定支架不正确并检测薄盖。加上 Tion，H-Ivus的高带宽提供了基本和谐波带，预计会产生基本带宽 证明了组织分类，如我们在颈动脉中所确定的。我们将使用宽带小波的分析 RF，图像中的空间结构和机器学习以确定宽带H-IVU是否可以提供改进 分割以提高对重要临床地标的认识并提供优质的自动斑块 对当前VHIVUS®的分类，该分类仅使用狭窄的RF-Funnauntal-Band固定光谱分析。 此外，我们的制造友好设计应大大降低成本，从而将这种障碍限制为UTILIZA- 特别是，我们将使用集中的聚合超声传感器开发基于导管的H-IVUS PCB。 开发利用宽带RF和谐波成像的算法来分析W组织类型和严格 将结果与常规IVU和IVOCT进行比较。我们的研究将提供许多创新，并启用 创建产品以破坏冠状动脉内成像。