Quantification of myocardial blood flow using Dynamic PET/CTA fused imagery to determine physiological significance of specific coronary lesions

使用动态 PET/CTA 融合图像对心肌血流量进行量化，以确定特定冠状动脉病变的生理意义

• 批准号: 10198024
• 负责人: Marina Piccinelli
• 金额: $ 52.8万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10198024
• 关键词: 3-Dimensional; Achievement; Algorithms; Anatomy; Automation; Blood Vessels; Blood flow; Bypass; Cardiac; Cardiac Catheterization Procedures; Caring; Catheterization; Cessation of life; Clinical; Code; Color; Computing Methodologies; Consumption; Coronary; Coronary Angiography; Coronary Arteriosclerosis; Coronary Stenosis; Coronary Vessels; Data; Databases; Decision Making; Detection; Diagnosis; Diagnostic; Drops; Evaluation; Goals; Health Care Costs; Image; Imagery; Impairment; Left; Left ventricular structure; Lesion; Location; Manuals; Masks; Measurement; Measures; Methodology; Methods; Morphologic artifacts; Myocardial; Myocardial perfusion; Myocardium; Non-Invasive Lesion; Operative Surgical Procedures; Pathway interactions; Patient Selection; Patient risk; Patient-Focused Outcomes; Patients; Performance; Perfusion; Physicians; Physiological; Positron-Emission Tomography; Principal Component Analysis; Procedures; Process; Radiation; Radiation exposure; Risk Assessment; Sampling; Selection for Treatments; Severities; Shapes; Software Tools; Stents; Surface; Thick; Three-Dimensional Image; Time; Trees; Unnecessary Procedures; Variant; Work; accurate diagnostics; algorithm development; base; clinical application; coronary lesion; cost; experience; image processing; improved; improved outcome; indexing; innovation; interest; multimodality; novel; patient variability; perfusion imaging; predict clinical outcome; pressure; prevent; software development; standard measure; tool

Project Summary One of every 6 deaths in the USA in 2015 was caused by coronary artery disease (CAD). Traditionally, primarily anatomic considerations have been used to diagnose CAD. Fractional flow reserve (FFR), a physiological index of blood-flow reduction caused by coronary stenosis, has been shown by the FAME trials as a better predictor of clinical outcomes from coronary revascularization than that based on anatomy alone. PET-derived absolute myocardial blood flow (MBF), flow reserve (MFR) and relative flow reserve (RFR) have been shown to add clinical value in detecting CAD and risk assessment. Currently, PET measurements of MBF, MFR and RFR are not lesion specific, calculated either globally for the entire left ventricle (LV), or regionally to pre-defined vascular or segmental territories. This approach is limited by the intermixing of normal flow from normal regions with abnormal flow from abnormal regions thus reducing the measured degree of flow-impairment, diagnostic performance and culpable lesion location. We and others have shown that the variability alone of vessel pathway between patients leads to 18% misdiagnosis rate. We propose to develop algorithms to non-invasively measure MBF, MFR and RFR across specific coronary lesions for the entire coronary tree at least as accurately as those measured invasively during cardiac catheterization using fused coronary anatomy data obtained from CT coronary angiography (CTA) with dynamic PET (dPET) flow physiologic data. We hypothesize that our novel 3D fusion dPET/CTA approach will accurately and non- invasively predict lesion-specific severity as defined by invasive coronary angiography (ICA) FFR obtained with flow-wire/pressure-wire approaches. We anticipate that our dPET/CTA approach will be significantly more accurate than other existing non-invasive approaches. Exploiting our achievements in algorithm development, we will pursue our specific aims of 1) automating CTA myocardial border and vessel segmentation, 2) automating dPET/CTA 3D fusion to localize myocardial volumes of interest (VOIs) on dPET studies corresponding to the anatomical path of coronary vessels from CTA, and 3) calculating MBF and related flow parameters along coronary vessels using clinically accepted PET flow methods. Our dPET/CTA method will result in the following game-changing paradigm: 1) eliminate unnecessary ICAs in patients with no significant lesions, 2) avoid stenting physiologically insignificant lesions, 3) guide the PCI process to the location of significant lesions, 4) provide a flow-color-coded 3D roadmap of the entire coronary tree to guide bypass surgery, and 5) use less radiation and lower cost.

项目摘要 2015年美国每6例死亡中的一次是由冠状动脉疾病（CAD）引起的。传统上， 主要是解剖因素已用于诊断CAD。分数流储备（FFR），一个 由冠状动脉狭窄引起的血液减少的生理指数已通过名声试验显示 作为对冠状动脉血运重建的临床结局的更好预测指标，而不是基于解剖结构。 宠物衍生的绝对心肌流动（MBF），流量储备（MFR）和相对流量储备（RFR）具有 被证明可以在检测CAD和风险评估中增加临床价值。目前，宠物测量值 MBF，MFR和RFR不是特定于病变的，在整个左心室（LV）或 在区域到预定义的血管或部分区域。这种方法受正常混合的限制 来自正常区域的流量异常，来自异常区域，从而降低了测得的程度 流动障碍，诊断性能和可屈服的病变位置。我们和其他人表明 患者之间的血管途径的可变性会导致18％的误诊率。我们建议发展 整个特定的冠状动脉病变，非侵入性测量MBF，MFR和RFR的算法 冠状树至少与使用融合的心脏导管期间在心脏导管期间的侵入式树一样准确 从CT冠状动脉造影（CTA）获得动态PET（DPET）流量的冠状动脉解剖学数据 生理数据。我们假设我们的新型3D融合DPET/CTA方法将准确而非 - 通过浸润性冠状动脉造影（ICA）FFR定义的侵入性病变特异性严重程度 使用流线/压线接近。我们预计我们的DPET/CTA方法将更大 比其他现有的非侵入性方法准确。利用我们在算法开发中的成就， 我们将追求我们的具体目标1）自动化CTA心肌边界和船只细分，2） 自动化DPET/CTA 3D融合以本地化感兴趣的心肌体积（VOI）在DPET研究中 对应于来自CTA的冠状动脉血管的解剖路径，3）计算MBF和相关流动 使用临床接受的宠物流量方法沿冠状动脉血管的参数。 我们的DPET/CTA方法将导致以下改变游戏规则的范式：1）消除不必要的 没有明显病变的患者的ICA，2）避免置于生理上微不足道的病变，3）指导 PCI过程到明显病变的位置，4）提供了整个流量彩色编码的3D路线图 指导旁路手术的冠状树，5）使用更少的辐射和较低的成本。

项目成果

Multimodality Image Fusion for Coronary Artery Disease Detection: Concepts and Latest Developments.

• DOI: 10.17996/anc.18-00065
• 发表时间: 2018-01-01
• 期刊: Annals of nuclear cardiology
• 作者: Piccinelli, Marina;Cooke, David C;Garcia, Ernest V
• 通讯作者: Garcia, Ernest V

Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies.

• DOI: 10.3390/jimaging6110125
• 发表时间: 2020-11-19
• 期刊: Journal of imaging
• 影响因子: 3.2
• 作者: Comelli A;Coronnello C;Dahiya N;Benfante V;Palmucci S;Basile A;Vancheri C;Russo G;Yezzi A;Stefano A
• 通讯作者: Stefano A

Directionally Paired Principal Component Analysis for Bivariate Estimation Problems.

• DOI: 10.1109/icpr48806.2021.9412245
• 发表时间: 2021-01
• 期刊: Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition
• 作者: Fan Y;Dahiya N;Bignardi S;Sandhu R;Yezzi A
• 通讯作者: Yezzi A

Accelerated Optimization in the PDE Framework Formulations for the Active Contour Case.

• DOI: 10.1137/19m1304210
• 发表时间: 2020
• 期刊: SIAM journal on imaging sciences
• 影响因子: 2.1
• 作者: Yezzi A;Sundaramoorthi G;Benyamin M
• 通讯作者: Benyamin M

Dynamic cardiac PET motion correction using 3D normalized gradient fields in patients and phantom simulations.

• DOI: 10.1002/mp.15059
• 发表时间: 2021-09
• 期刊: Medical physics
• 影响因子: 3.8
• 作者: Nye JA;Piccinelli M;Hwang D;David Cooke C;Paeng JC;Lee JM;Cho SG;Folks R;Bom HS;Koo BK;Garcia EV
• 通讯作者: Garcia EV