Clinically viable myocardial CCTA segmentation for measuring vessel-specific myocardial blood flow from dynamic PET/CCTA hybrid fusion.
Clinically viable myocardial CCTA segmentation for measuring vessel-specific myocardial blood flow from dynamic PET/CCTA hybrid fusion.
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DOI：
10.1186/s41824-021-00122-1
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发表时间：
2022-02-15
影响因子：
1.7
通讯作者：
Garcia EV
中科院分区：
文献类型：
--
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
正电子发射断层扫描（PET）衍生的LV MBF定量通常在标准解剖血管区域中测量，可能对正常灌注组织的血流与异常血流供应区域的血流进行平均。以前，我们报道了一种基于图像的工具，用于无创测量单个心外膜血管下方位置的绝对心肌血流量，以帮助指导血运重建。本工作的目的是确定从动态PET（dPET）与冠状动脉计算机断层扫描血管造影术（CCTA）心肌分割的融合中提取的血管特异性流量测量（MBFvs）的稳健性，使用从与CCTA手动分割的融合中测量的流量作为参考标准。43名患者的13 NH3 dPET、CCTA图像数据集用于测量dPET数据与三种CCTA解剖模型融合后MBFvs曲线的一致性：（1）手动模型，（2）全自动分割模型和（3）校正模型，其中对自动分割中的主要不准确性进行了简要编辑。沿沿着不同提取血管的流量值的正常/异常一致性的成对准确度通过逐点比较每个血管的流量与相应血管的正常限值（使用Dice系数（DC）作为度量）来确定。在43名患者的CCTA全自动掩模模型中，27名患者的边界需要在dPET/CCTA图像融合之前进行手动校正，但该编辑过程很短（2-3分钟），可以在临床可接受的时间内100%成功提取MBFvs。在使用手动和校正CCTA面罩模型进行dPET融合后，共分析了124条血管，得到2225个应力和2122个静息血流值。47支血管在融合后用全自动面罩进行分析，产生840个应力和825个静息血流样本。全球或按地区计算的所有DC系数均≥ 0.93。手动和校正或手动和全自动CCTA面罩之间的正常/异常血流分类无统计学差异。全自动和手动校正的心肌CCTA分割在临床可接受的时间内提供解剖掩膜，用于使用动态PET/CCTA图像融合进行血管特异性心肌血流测量，与全手动分割的CCTA心肌掩膜相比，动态PET/CCTA图像融合在血流准确度方面没有显著差异，并且在临床可接受的处理时间内。
Positron emission tomography (PET)-derived LV MBF quantification is usually measured in standard anatomical vascular territories potentially averaging flow from normally perfused tissue with those from areas with abnormal flow supply. Previously we reported on an image-based tool to noninvasively measure absolute myocardial blood flow at locations just below individual epicardial vessel to help guide revascularization. The aim of this work is to determine the robustness of vessel-specific flow measurements (MBFvs) extracted from the fusion of dynamic PET (dPET) with coronary computed tomography angiography (CCTA) myocardial segmentations, using flow measured from the fusion with CCTA manual segmentation as the reference standard. Forty-three patients’ 13NH3 dPET, CCTA image datasets were used to measure the agreement of the MBFvs profiles after the fusion of dPET data with three CCTA anatomical models: (1) a manual model, (2) a fully automated segmented model and (3) a corrected model, where major inaccuracies in the automated segmentation were briefly edited. Pairwise accuracy of the normality/abnormality agreement of flow values along differently extracted vessels was determined by comparing, on a point-by-point basis, each vessel’s flow to corresponding vessels’ normal limits using Dice coefficients (DC) as the metric. Of the 43 patients CCTA fully automated mask models, 27 patients’ borders required manual correction before dPET/CCTA image fusion, but this editing process was brief (2–3 min) allowing a 100% success rate of extracting MBFvs in clinically acceptable times. In total, 124 vessels were analyzed after dPET fusion with the manual and corrected CCTA mask models yielding 2225 stress and 2122 rest flow values. Forty-seven vessels were analyzed after fusion with the fully automatic masks producing 840 stress and 825 rest flow samples. All DC coefficients computed globally or by territory were ≥ 0.93. No statistical differences were found in the normal/abnormal flow classifications between manual and corrected or manual and fully automated CCTA masks. Fully automated and manually corrected myocardial CCTA segmentation provides anatomical masks in clinically acceptable times for vessel-specific myocardial blood flow measurements using dynamic PET/CCTA image fusion which are not significantly different in flow accuracy and within clinically acceptable processing times compared to fully manually segmented CCTA myocardial masks.
通过融合动力学（13）NH（3）PET和CCTA：在低风险人群中的范围和异常标准的范围，对绝对心肌流动，心肌流量储备和相对流量储备的特定定量定量。
DOI：
10.1007/s12350-018-01472-3
发表时间：
2020-10
期刊：
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
影响因子：
--
作者：
Piccinelli M;Cho SG;Garcia EV;Alexanderson E;Lee JM;Cooke CD;Goyal N;Sanchez MS;Folks RD;Chen Z;Votaw J;Koo BK;Bom HS
通讯作者：
Bom HS