title: Automated vessel segmentation using cross-correlation and pooled covariance matrix analysis.
abstract: Time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA) provides contrast dynamics in the vasculature and allows vessel segmentation based on temporal correlation analysis. Here we present an automated vessel segmentation algorithm including automated generation of regions of interest (ROIs), cross-correlation and pooled sample covariance matrix analysis. The dynamic images are divided into multiple equal-sized regions. In each region, ROIs for artery, vein and background are generated using an iterative thresholding algorithm based on the contrast arrival time map and contrast enhancement map. Region-specific multi-feature cross-correlation analysis and pooled covariance matrix analysis are performed to calculate the Mahalanobis distances (MDs), which are used to automatically separate arteries from veins. This segmentation algorithm is applied to a dual-phase dynamic imaging acquisition scheme where low-resolution time-resolved images are acquired during the dynamic phase followed by high-frequency data acquisition at the steady-state phase. The segmented low-resolution arterial and venous images are then combined with the high-frequency data in k-space and inverse Fourier transformed to form the final segmented arterial and venous images. Results from volunteer and patient studies demonstrate the advantages of this automated vessel segmentation and dual phase data acquisition technique.
authors.affiliation: Department of Radiology, University of California, San Diego, San Diego, CA 92103-8226, USA. jiangdu@ucsd.edu
authors.forename: Jiang
authors.initials: J
authors.lastname: Du
authors.forename: Afshin
authors.initials: A
authors.lastname: Karimi
authors.forename: Yijing
authors.initials: Y
authors.lastname: Wu
authors.forename: Frank R
authors.initials: FR
authors.lastname: Korosec
authors.forename: Thomas M
authors.initials: TM
authors.lastname: Grist
authors.forename: Charles A
authors.initials: CA
authors.lastname: Mistretta
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D001808:Blood Vessels / Q000033:anatomy & histology*; D003287:Contrast Media; D003627:Data Interpretation, Statistical; D005682:Gadolinium; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic
chemical_list: D003287:Contrast Media; D005682:Gadolinium