Abstract
PurposePrecise quantification of cerebral arteries can help with differentiation and prognostication of cerebrovascular disease. Existing image processing and segmentation algorithms for magnetic resonance angiography (MRA) are limited to the analysis of either 2D maximum intensity projection images or the entire 3D volume. The goal of this study was to develop a fully automated, hybrid 2D-3D method for robust segmentation of arteries and accurate quantification of vessel radii using MRA at varying projection thicknesses.MethodsA novel algorithm that employs an adaptive Frangi filter for segmentation of vessels followed by estimation of vessel radii is presented. The method was evaluated on MRA datasets and corresponding manual segmentations from three healthy subjects for various projection thicknesses. In addition, the vessel metrics were computed in four additional subjects. Three synthetically generated angiographic datasets resembling brain vasculature were also evaluated under different noise levels. Dice similarity coefficient, Jaccard Index, F-score, and concordance correlation coefficient were used to measure the segmentation accuracy of manual versus automatic segmentation.ResultsOur new adaptive filter rendered accurate representations of vessels, maintained accurate vessel radii, and corresponded better to manual segmentation at different projection thicknesses than prior methods. Validation with synthetic datasets under low contrast and noisy conditions revealed accurate quantification of vessels without distortions.ConclusionWe have demonstrated a method for automatic segmentation of vascular trees and the subsequent generation of a vessel radii map. This novel technique can be applied to analyze arterial structures in healthy and diseased populations and improve the characterization of vascular integrity.
Highlights
Maximum intensity projection (MIP) images (Arlart et al, 1995; Johnson et al, 1998) taken through the 3D image volumes acquired using a TOF-MRA sequence provide a more informative visual display for analysis of vessels and are typically used for segmentation
To overcome the above-mentioned limitations, we introduce a novel hybrid approach which we call an adaptive Frangi technique that incorporates a Euclidean distance transform (EDT) with the Frangi filter in order to preserve accurate vessel radii information
The corresponding vessel radii distribution for the two volunteers is shown, whereby the vessel radii map for the MIP image is color coded in terms of the number of pixels thick, where 1 pixel = 0.23 mm
Summary
Maximum intensity projection (MIP) images (Arlart et al, 1995; Johnson et al, 1998) taken through the 3D image volumes acquired using a TOF-MRA sequence provide a more informative visual display for analysis of vessels and are typically used for segmentation. MIP is a volume rendering technique for 3D data that selects the maximum voxel value along a line from the viewpoint to the plane of projection. The presence of overlapping non-vascular structures greatly affects the visualization of small vessels with low contrast, especially at larger projection thicknesses. To overcome these issues, vessel enhancement algorithms can be first applied in order to suppress non-vascular structures and improve delineation of small blood vessels
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