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例死亡中就有1例是由冠状动脉疾病（CAD）引起的。传统上， 主要的解剖学考虑已经用于诊断CAD。血流储备分数（FFR），a FAME试验表明，冠状动脉狭窄导致血流减少的生理指标 作为冠状动脉血运重建的临床结果的更好的预测比单独基于解剖。 PET导出的绝对心肌血流量（MBF）、血流储备（MFR）和相对血流储备（RFR） 在检测CAD和风险评估方面增加了临床价值。目前，PET测量 MBF、MFR和RFR不是病变特异性的，可以整体计算整个左心室（LV），或 区域性地到预定义的血管或节段区域。这种方法受到正常的混合的限制， 来自正常区域的流量与来自异常区域的异常流量，从而降低了测量的 血流障碍、诊断性能和应受谴责的病变位置。我们和其他人已经表明， 单是患者间血管通路的变异性就导致了18%的误诊率。我们建议发展 用于非侵入性测量特定冠状动脉病变的MBF、MFR和RFR的算法， 冠状动脉树至少与使用融合的心脏导管插入术期间侵入性测量的冠状动脉树一样准确 通过CT冠状动脉造影（CTA）和动态PET（dPET）血流获得的冠状动脉解剖数据 生理数据我们假设，我们的新型3D融合dPET/CTA方法将准确且非 通过有创冠状动脉造影（伊卡）获得的FFR定义的有创预测病变特异性严重程度 使用流动导丝/压力导丝方法。我们预计，我们的dPET/CTA方法将大大超过 比现有的非侵入性方法更准确。利用我们在算法开发方面的成就， 我们将追求我们的具体目标：1）自动化CTA心肌边界和血管分割，2） 自动化dPET/CTA 3D融合，以在dPET研究中定位感兴趣的心肌体积（VOI） 对应于来自CTA的冠状动脉血管的解剖路径;以及3）计算MBF和相关流量 使用临床上接受的PET流动方法沿冠状血管的沿着参数。 我们的dPET/CTA方法将导致以下改变游戏规则的范例：1）消除不必要的 无明显病变患者的ICA，2）避免支架植入生理学上无意义的病变，3）引导 PCI过程到显著病变的位置，4）提供整个PCI过程的血流彩色编码3D路线图， 冠状动脉树引导搭桥手术; 5）使用较少的辐射和较低的费用。

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

Clinically viable myocardial CCTA segmentation for measuring vessel-specific myocardial blood flow from dynamic PET/CCTA hybrid fusion.

• DOI: 10.1186/s41824-021-00122-1
• 发表时间: 2022-02-15
• 期刊: European journal of hybrid imaging
• 影响因子: 1.7
• 作者: Piccinelli M;Dahiya N;Nye JA;Folks R;Cooke CD;Manatunga D;Hwang D;Paeng JC;Cho SG;Lee JM;Bom HS;Koo BK;Yezzi A;Garcia EV
• 通讯作者: Garcia EV