Automatic Model-Based Contour Detection and Blood Flow Quantification in Small Vessels with Velocity Encoded Magnetic Resonance Imaging

Rationale and Objectives:The quantitative assessment of blood flow in peripheral vessels from phase-contrast magnetic resonance imaging studies requires the accurate delineation of vessel contours in cross-sectional magnetic resonance images. The conventional manual segmentation approach is tedious, time-consuming, and leads to significant inter- and intraobserver variabilities. The aim of this study was to verify whether automatic model-based segmentation decreases these problems by fitting a model to the actual blood velocity profile. Methods:In this study 2 new fully automatic methods (a static and a dynamic approach) were developed and compared with manual analyzes using phantom and in vivo studies of internal carotid and vertebral arteries in healthy volunteers. The automatic segmentation approaches were based on fitting a 3D parabolic velocity model to the actual velocity profiles. In the static method, the velocity profiles were averaged over the complete cardiac cycle, whereas the dynamic method takes into account the velocity data of each cardiac time bin individually. Materials consisted of the magnetic resonance imaging data from 3 straight phantom tubes and the blood velocity profiles of 8 volunteers. Results:For the phantom studies, the automatic dynamic approach performed significantly better than the manual analysis (intraclass correlations [ICC] of 0.62–0.98 and 0.30–0.86, respectively). For the assessment of the total cerebral blood flow in the in vivo studies, the automatic static method performed significantly better than the manual 1 (ICC of 0.98–0.98 and 0.93–0.95, respectively). However, the automatic dynamic method was not significantly better than the manual 1 (ICC = 0.92–0.96) but had the advantage of providing additional parameters. Conclusion:Blood flow in magnetic resonance images of small vessels can be assessed accurately, rapidly, and fully automatically using model-based postprocessing by fitting a first approximation of the velocity profile to the actual flow data.

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