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.

摘要 冠状动脉内成像是一种强大的临床工具，用于决策，治疗计划和评估 支架展开。它也是一个强大的研究工具，用于斑块进展/消退，药物治疗， 器械干预。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）提供了良好的分辨率，并允许测量管腔狭窄、壁厚、管腔狭窄和管腔狭窄。 增厚、动脉粥样化负担以及较小程度的支架展开。利用射频信号的频谱分析 和机器学习，我们的团队开发了软件，后来被商业化， 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） 分辨率优于IVUS，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，能够评估总的 牙菌斑负担此外，与IVOCT不同，IVUS不需要在植入前冲洗血管中的血液。 考虑到肾脏疾病的患病率，成像对于患者来说是一个重要的问题。这些限制表明， 对具有IVUS和IVOCT属性的新血管内成像模式的未满足需求。 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）， 将满足IVOCT和传统IVUS确定的临床需求。H-IVUS的分辨率接近IVOCT 为了能够识别关键的小结构（例如，薄帽和支架支柱），同时保持 超声穿透组织和评估软斑块负担。它将立即产生临床影响， 帮助临床医生规划和优化已经显示受益于血管内成像的手术： 确定真实血管尺寸，识别支架着陆区以选择正确的支架长度，识别斑块形态- 引导减积、检测边缘夹层、确定支架贴壁不良和检测薄帽的ogies。此外， 因此，H-IVUS的高带宽提供了基波和谐波频带，预计将改善 证明组织分类，如我们在颈动脉中确定的。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，并提供上级自动斑块 VHIVUS ®仅使用窄RF基频带固定频谱分析。 此外，我们的制造友好型设计应大大降低成本，从而限制这一障碍的利用， 是的。具体而言，我们将开发一种基于导管的H-IVUS PCB，使用聚焦聚合物超声换能器; 开发利用宽带RF和谐波成像的算法，以严格分析组织类型 将结果与常规IVUS和IVOCT进行比较。我们的研究将提供许多创新， 创建一种产品来破坏冠状动脉内成像。