Ding Xiaowei, Terzopoulos Demetri, Diaz-Zamudio Mariana, Berman Daniel S, Slomka Piotr J, Dey Damini
Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, California 90048 and Computer Science Department, Henry Samueli School of Engineering and Applied Science at UCLA, Los Angeles, California 90095.
Computer Science Department, Henry Samueli School of Engineering and Applied Science at UCLA, Los Angeles, California 90095.
Med Phys. 2015 Sep;42(9):5015-26. doi: 10.1118/1.4927375.
The authors aimed to develop and validate an automated algorithm for epicardial fat volume (EFV) quantification from noncontrast CT.
The authors developed a hybrid algorithm based on initial segmentation with a multiple-patient CT atlas, followed by automated pericardium delineation using geodesic active contours. A coregistered segmented CT atlas was created from manually segmented CT data and stored offline. The heart and pericardium in test CT data are first initialized by image registration to the CT atlas. The pericardium is then detected by a knowledge-based algorithm, which extracts only the membrane representing the pericardium. From its initial atlas position, the pericardium is modeled by geodesic active contours, which iteratively deform and lock onto the detected pericardium. EFV is automatically computed using standard fat attenuation range.
The authors applied their algorithm on 50 patients undergoing routine coronary calcium assessment by CT. Measurement time was 60 s per-patient. EFV quantified by the algorithm (83.60 ± 32.89 cm(3)) and expert readers (81.85 ± 34.28 cm(3)) showed excellent correlation (r = 0.97, p < 0.0001), with no significant differences by comparison of individual data points (p = 0.15). Voxel overlap by Dice coefficient between the algorithm and expert readers was 0.92 (range 0.88-0.95). The mean surface distance and Hausdorff distance in millimeter between manually drawn contours and the automatically obtained contours were 0.6 ± 0.9 mm and 3.9 ± 1.7 mm, respectively. Mean difference between the algorithm and experts was 9.7% ± 7.4%, similar to interobserver variability between 2 readers (8.0% ± 5.3%, p = 0.3).
The authors' novel automated method based on atlas-initialized active contours accurately and rapidly quantifies EFV from noncontrast CT.
作者旨在开发并验证一种用于从非增强CT中定量测量心外膜脂肪体积（EFV）的自动化算法。
作者开发了一种混合算法，该算法首先基于多患者CT图谱进行初始分割，然后使用测地线活动轮廓自动勾勒心包。从手动分割的CT数据创建一个配准的分割CT图谱并离线存储。测试CT数据中的心脏和心包首先通过图像配准到CT图谱进行初始化。然后通过基于知识的算法检测心包，该算法仅提取代表心包的膜。从其在图谱中的初始位置开始，心包通过测地线活动轮廓进行建模，该轮廓迭代变形并锁定到检测到的心包上。使用标准脂肪衰减范围自动计算EFV。
作者将他们的算法应用于50例接受CT常规冠状动脉钙化评估的患者。每位患者的测量时间为60秒。该算法定量得出的EFV（83.60±32.89 cm³）与专家读者测量的结果（81.85±34.28 cm³）显示出极佳的相关性（r = 0.97，p < 0.0001），通过比较单个数据点无显著差异（p = 0.15）。算法与专家读者之间的Dice系数体素重叠率为0.92（范围为0.88 - 0.95）。手动绘制轮廓与自动获得的轮廓之间的平均表面距离和豪斯多夫距离（以毫米为单位）分别为0.6±0.9毫米和3.9±1.7毫米。算法与专家之间的平均差异为9.7%±7.4%，类似于两位读者之间的观察者间变异性（8.0%±5.3%，p = 0.3）。
作者基于图谱初始化活动轮廓的新型自动化方法能够准确、快速地从非增强CT中定量测量EFV。
