Quantification of common carotid artery and descending aorta vessel wall thickness from MR vessel wall imaging using a fully automated processing pipeline

• Published in: Journal of magnetic resonance imaging Vol. 45; no. 1; pp. 215 - 228
• Main Authors: Gao, Shan, van 't Klooster, Ronald, Brandts, Anne, Roes, Stijntje D., Alizadeh Dehnavi, Reza, de Roos, Albert, Westenberg, Jos J.M., Geest, Rob J.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.01.2017
• Subjects: 3D segmentation; Algorithms; aorta; Aorta, Thoracic - anatomy & histology; Aorta, Thoracic - diagnostic imaging; carotid; Carotid Artery, Common - anatomy & histology; Carotid Artery, Common - diagnostic imaging; Female; fully automatic; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Machine Learning; Magnetic Resonance Angiography - methods; Magnetic resonance imaging; Male; Middle Aged; MRI; Organ Size; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; wall thickness; 3D segmentation; aorta; MRI; wall thickness; carotid; fully automatic
• ISSN: 1053-1807; 1522-2586
• DOI: 10.1002/jmri.25332

Purpose To develop and evaluate a method that can fully automatically identify the vessel wall boundaries and quantify the wall thickness for both common carotid artery (CCA) and descending aorta (DAO) from axial magnetic resonance (MR) images. Materials and Methods 3T MRI data acquired with T1‐weighted gradient‐echo black‐blood imaging sequence from carotid (39 subjects) and aorta (39 subjects) were used to develop and test the algorithm. The vessel wall segmentation was achieved by respectively fitting a 3D cylindrical B‐spline surface to the boundaries of lumen and outer wall. The tube‐fitting was based on the edge detection performed on the signal intensity (SI) profile along the surface normal. To achieve a fully automated process, Hough Transform (HT) was developed to estimate the lumen centerline and radii for the target vessel. Using the outputs of HT, a tube model for lumen segmentation was initialized and deformed to fit the image data. Finally, lumen segmentation was dilated to initiate the adaptation procedure of outer wall tube. The algorithm was validated by determining: 1) its performance against manual tracing; 2) its interscan reproducibility in quantifying vessel wall thickness (VWT); 3) its capability of detecting VWT difference in hypertensive patients compared with healthy controls. Statistical analysis including Bland–Altman analysis, t‐test, and sample size calculation were performed for the purpose of algorithm evaluation. Results The mean distance between the manual and automatically detected lumen/outer wall contours was 0.00 ± 0.23/0.09 ± 0.21 mm for CCA and 0.12 ± 0.24/0.14 ± 0.35 mm for DAO. No significant difference was observed between the interscan VWT assessment using automated segmentation for both CCA (P = 0.19) and DAO (P = 0.94). Both manual and automated segmentation detected significantly higher carotid (P = 0.016 and P = 0.005) and aortic (P < 0.001 and P = 0.021) wall thickness in the hypertensive patients. Conclusion A reliable and reproducible pipeline for fully automatic vessel wall quantification was developed and validated on healthy volunteers as well as patients with increased vessel wall thickness. This method holds promise for helping in efficient image interpretation for large‐scale cohort studies. Level of Evidence: 4 J. Magn. Reson. Imaging 2017;45:215–228.