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.

摘要 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 支架置入。它也是斑块进展/消退、药物治疗和 设备干预。常见的血管内成像方法各有优缺点。 血管内超声(IVUS)提供了良好的分辨率，并允许测量管腔狭窄、管壁 增厚、动脉粥样硬化负担，以及较小程度的支架置入。使用射频信号的频谱分析 和机器学习，我们团队开发了软件，后来被商业化，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) 比静脉内超声分辨率更高，能够可视化和分析支架支柱、易损斑块的薄帽、 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 斑块负担。此外，与IVOCT不同的是，IVUS不需要在以下时间之前从血管中冲洗血液 鉴于肾脏疾病的流行，成像对患者来说是一个重要的问题。这些限制表明， 对同时具有IVUS和IVOCT属性的新血管内成像方式的需求尚未得到满足。 我们将创造一种新型的血管内，高频，宽带，聚焦超声系统(H-IVUS)，它 将解决IVOCT和传统IVUS确定的临床需求。H-IVUS的分辨率将接近IVOCT 能够识别关键的小结构(例如，薄帽和支架支柱)，同时保持能力 超声穿透组织并评估软斑块负荷。它将立即对ENA产生临床影响- 鼓励临床医生计划和优化已经证明受益于血管内成像的程序： 确定真实的血管大小，确定支架着陆区以选择正确的支架长度，确定斑块吗啡- OGES用于指导去除支架、检测边缘剥离、确定支架错位和检测薄帽。另外-- 此外，H-IVUS的高带宽同时提供了基波和谐波两个频段，这有望促进H-IVUS的发展。 证明组织分类，由我们在颈动脉中确定。我们将使用宽带小波分析 射频、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物的识别并提供卓越的自动化斑块 与当前的VH IVUS®相比，VH IVUS®仅使用窄的RF基带固定频谱分析。 此外，我们易于制造的设计应该会大大降低成本，从而将这一障碍限制在利用- 提顿。具体地说，我们将开发一种基于导管的H-IVUS印刷电路板，使用聚焦聚合物超声换能器； 开发利用宽带射频和谐波成像来严格分析组织类型的算法 将结果与传统的IVUS和IVOCT进行比较。我们的研究将提供许多创新并使 创造了一种扰乱冠状动脉内成像的产品。