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

项目摘要 2015年，美国每6例死亡中就有1例是由冠心病(CAD)引起的。传统上， 主要用于诊断CAD的是解剖学方面的考虑。分数流量储备(FFR)，a 冠状动脉狭窄导致血流减少的生理指标已被FAME试验所证实 作为冠状动脉血管重建术的临床结果的更好的预测因子，而不是仅仅基于解剖学的结果。 PET衍生的心肌绝对血流量(MBF)、血流储备(MFR)和相对血流储备(RFR)有 已被证明在检测CAD和风险评估方面具有临床价值。目前，PET测量的 MBF、MFR和RFR不是病变特异性的，要么是针对整个左心室(LV)整体计算的，要么是 从局部到预定义的血管或节段性区域。此方法受限于Normal 来自正常区域的流量与来自异常区域的异常流量，从而降低了测量的程度 血流障碍、诊断表现和有罪的病变部位。我们和其他人已经证明 仅患者间血管通路的变异性就会导致18%的误诊率。我们建议开发 无创测量特定冠状动脉病变的MBF、MFR和RFR的算法 冠脉树的精确度至少与使用FUSED进行心导管插管时的有创性测量结果相同 动态PET(DPET)血流CT冠状动脉成像(CTA)获得的冠状动脉解剖数据 生理数据。我们假设我们的新的3D融合dPET/CTA方法将准确和非 根据有创冠状动脉造影术(ICA)获得的FFR对特定病变严重程度进行侵入性预测 采用流线/压线方法。我们预计我们的dPET/CTA方法将显著 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就， 我们将实现我们的具体目标：1)自动分割CTA心肌边界和血管，2) 自动dPET/CTA 3D融合在dPET研究中定位感兴趣心肌体积(VOI) 与CTA冠状动脉的解剖路径相对应；3)计算MBF及相关流量 应用临床公认的正电子发射计算机断层扫描(PET)血流方法测量冠状动脉参数。 我们的dPET/CTA方法将产生以下改变游戏规则的范例：1)消除不必要的 ICAS在无明显病变的患者中，2)避免支架植入生理上无意义的病变，3)引导 4)提供血流彩色编码的整体3D路线图。 冠状动脉树指导搭桥手术，5)使用较少的辐射和较低的成本。

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.