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