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Automated quantitative 3D analysis of aorta size, morphology, and mural calcification distributions
Sila Kurugol1, Carolyn E Come1, Alejandro A Diaz1
1Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115.
Medical Physics
|September 3, 2015
Summary
A new automated pipeline accurately measures aorta morphology and calcification on CT scans. This tool may identify cardiovascular disease risks in smokers by assessing aorta changes with aging and calcification.
Area of Science:
- Medical Imaging
- Cardiovascular Disease Research
- Computational Anatomy
Background:
- Cardiovascular disease (CVD) poses a significant health burden.
- Aorta morphology and calcification are potential imaging biomarkers for CVD.
- Accurate and automated measurement tools are needed for large-scale studies.
Purpose of the Study:
- To develop a fully automated pipeline for computing aorta morphology and calcification measures from CT scans.
- To investigate the utility of these measures as imaging biomarkers for cardiovascular disease.
Main Methods:
- Aorta segmentation using 3D level-set segmentation refined by edge detection.
- Aortic calcification detection via thresholding with anatomical false positive filtering.
- Extraction of aorta centerline and cross-sections to compute morphology (e.g., area, tortuosity) and calcification (e.g., volume, number of plaques) measures.
Main Results:
- High agreement between automated segmentation and expert measurements (Dice coefficient 0.92 ± 0.01).
- Improved true positive detection rate for calcifications (0.96) compared to previous methods.
- Observed associations between aorta morphology, calcification, and aging, suggesting potential aorta stiffening and unwrapping.
Conclusions:
- An objective, automated tool for assessing aorta morphology and calcification on CT scans has been developed.
- This tool can provide valuable information regarding cardiovascular disease presence and impact, particularly in smokers.
- The findings support the use of aorta imaging biomarkers in clinical practice and research.
