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.

项目概要 2015 年，美国每 6 例死亡中就有 1 例死于冠状动脉疾病 (CAD)。传统上， 主要是根据解剖学考虑来诊断 CAD。血流储备分数 (FFR) FAME试验表明，冠状动脉狭窄引起的血流量减少的生理指标 与仅基于解剖学的预测相比，它可以更好地预测冠状动脉血运重建的临床结果。 PET 得出的绝对心肌血流量 (MBF)、血流储备 (MFR) 和相对血流储备 (RFR) 已被证明可以增加检测 CAD 和风险评估的临床价值。目前，PET 测量 MBF、MFR 和 RFR 不是特定于病变的，可以针对整个左心室 (LV) 进行全局计算，或者 区域性到预先定义的血管或节段区域。这种方法受到正常混合的限制 来自正常区域的流量和来自异常区域的异常流量，从而降低了测量的程度 血流损伤、诊断性能和罪魁祸首的位置。我们和其他人已经证明， 仅患者之间血管通路的变异性就导致 18% 的误诊率。我们建议开发 非侵入性测量整个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心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 根据侵入性冠状动脉造影 (ICA) 获得的 FFR 定义，侵入性预测病变特异性严重程度 采用流线/压力线方法。我们预计我们的 dPET/CTA 方法将显着更多 比其他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果， 我们将追求以下具体目标：1) 自动化 CTA 心肌边界和血管分割，2) 自动化 dPET/CTA 3D 融合以定位 dPET 研究中感兴趣的心肌体积 (VOIs) 对应于CTA的冠状血管解剖路径，3）计算MBF和相关流量 使用临床上接受的 PET 血流方法测量沿冠状血管的参数。 我们的 dPET/CTA 方法将带来以下改变游戏规则的范例：1）消除不必要的 无明显病变的 ICA 患者，2) 避免对生理上不明显病变进行支架植入，3) 指导 PCI 过程定位显着病变的位置，4) 提供整个流程的流程颜色编码 3D 路线图 冠状动脉树来指导搭桥手术，5) 使用更少的辐射和更低的成本。

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