Automated quantitative 3D analysis of aorta size, morphology, and mural calcification distributions

Abstract

Purpose: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

FIG. 1.

Flow chart of the fully automated pipeline.

FIG. 2.

(a) Red rectangle shows the VOI boundaries in anterior–posterior and left–right directions. (b) Left panel shows the axial slice of trachea bifurcation, the center of ascending aorta (blue plus), and the coronal slice location (yellow line). Right panel shows the coronal slice and the detected inferior boundary for the VOI (red line).

FIG. 3.

(a) In the slice of main carina, detected DA and AA circles and their centers are shown in red (circles and crosses), the (vertical) line through these centers is shown in blue and the second (horizontal) line normal to the first one is shown in yellow. (b) The vertical plane taken from the location of yellow line is shown together with some example oblique planes obtained by rotating that vertical plane. The circles detected sequentially in these oblique planes are indicated by red. (c) Circles detected in all consecutive oblique planes are shown.

FIG. 4.

The initial aorta boundaries (blue: Before Level Set) and the boundaries after level-set refinement (red: After Level Set), and the expert boundary (green: Expert) are shown. For slices where the cross section is not perfectly circular, the refinement step moves the boundaries to the nearby edge locations.

FIG. 5.

Examples of regions that are true calcifications [(b) and (d)] and false positives [(a) and (c)] after thresholding step. The algorithm further processes these regions and rejects the false positives.

FIG. 6.

(a) Segmented aorta and extracted centerline (blue). At the location of trachea carina, aorta arch width is measured (black arrow between two yellow circles). (b) Oblique aorta cross sections normal to centerline (green). (c) Axial planes (red) are added for reference. (d) Detected calcifications (blue).

FIG. 7.

Mean ± sd of error (top figure), Jaccard coefficient (middle figure), and Dice coefficient (bottom figure) for each data set.

FIG. 8.

Segmented aorta in 3D for two sample CT scans. Colormap indicates the closest point Euclidean distance (in mm) between automatically segmented and manually labeled aorta surfaces.

FIG. 9.

The left image shows all the detected circles in the slice of main carina, middle and right images show the detected AA and DA circles, respectively. The pink dot shows the location of carina.