Kurugol Sila, Come Carolyn E, Diaz Alejandro A, Ross James C, Kinney Greg L, Black-Shinn Jennifer L, Hokanson John E, Budoff Matthew J, Washko George R, San Jose Estepar Raul
Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115.
Colorado School of Public Health, University of Colorado Denver, Aurora, Colorado 80045.
Med Phys. 2015 Sep;42(9):5467-78. doi: 10.1118/1.4924500.
The purpose of this work is to develop a fully automated pipeline to compute aorta morphology and calcification measures in large cohorts of CT scans that can be used to investigate the potential of these measures as imaging biomarkers of cardiovascular disease.
The first step of the automated pipeline is aorta segmentation. The algorithm the authors propose first detects an initial aorta boundary by exploiting cross-sectional circularity of aorta in axial slices and aortic arch in reformatted oblique slices. This boundary is then refined by a 3D level-set segmentation that evolves the boundary to the location of nearby edges. The authors then detect the aortic calcifications with thresholding and filter out the false positive regions due to nearby high intensity structures based on their anatomical location. The authors extract the centerline and oblique cross sections of the segmented aortas and compute the aorta morphology and calcification measures of the first 2500 subjects from COPDGene study. These measures include volume and number of calcified plaques and measures of vessel morphology such as average cross-sectional area, tortuosity, and arch width.
The authors computed the agreement between the algorithm and expert segmentations on 45 CT scans and obtained a closest point mean error of 0.62 ± 0.09 mm and a Dice coefficient of 0.92 ± 0.01. The calcification detection algorithm resulted in an improved true positive detection rate of 0.96 compared to previous work. The measurements of aorta size agreed with the measurements reported in previous work. The initial results showed associations of aorta morphology with calcification and with aging. These results may indicate aorta stiffening and unwrapping with calcification and aging.
The authors have developed an objective tool to assess aorta morphology and aortic calcium plaques on CT scans that may be used to provide information about the presence of cardiovascular disease and its clinical impact in smokers.
本研究旨在开发一种全自动流程，用于在大量CT扫描数据中计算主动脉形态和钙化指标，以探讨这些指标作为心血管疾病影像生物标志物的潜力。
自动流程的第一步是主动脉分割。作者提出的算法首先通过利用轴向切片中主动脉的横截面圆形特征以及重组斜切片中主动脉弓的特征来检测初始主动脉边界。然后通过三维水平集分割对该边界进行细化，使边界向附近边缘的位置演化。作者随后通过阈值化检测主动脉钙化，并根据其解剖位置滤除由于附近高强度结构导致的假阳性区域。作者提取分割后主动脉的中心线和斜截面，并计算来自慢性阻塞性肺疾病基因（COPDGene）研究的前2500名受试者的主动脉形态和钙化指标。这些指标包括钙化斑块的体积和数量以及血管形态指标，如平均横截面积、迂曲度和弓宽度。
作者计算了该算法与45例CT扫描的专家分割之间的一致性，获得最接近点平均误差为0.62±0.09毫米，骰子系数为0.92±0.01。与先前的工作相比，钙化检测算法使真阳性检测率提高到了0.96。主动脉大小的测量结果与先前工作中报告的测量结果一致。初步结果显示主动脉形态与钙化以及衰老之间存在关联。这些结果可能表明随着钙化和衰老，主动脉变硬并展开。
作者开发了一种客观工具，用于评估CT扫描上的主动脉形态和主动脉钙斑，可用于提供有关吸烟者心血管疾病的存在及其临床影响的信息。
