Automated quantification of arterial stenosis on CE-MRA by using a deformable vascular tubular model
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POSTER PRESENTATION
Open Access
Automated quantification of arterial stenosis on CE-MRA by using a deformable vascular tubular model Avan Suinesiaputra1, Patrick JH Koning1, Elena Zudilova-Seinstra2, Johan HC Reiber1, Rob J van der Geest1* From 2011 SCMR/Euro CMR Joint Scientific Sessions Nice, France. 3-6 February 2011 Introduction Accurate arterial stenosis quantification is important for the decision of a proper treatment in patients suffering atherosclerotic disease. We have developed an automated arterial stenosis quantification method by using a deformable tubular 3D model that fits into luminal vasculature particularly in severe stenoses. Purpose To provide an automated analysis of arterial stenosis grading with minimal user-interaction. Methods Contrast-enhanced MRA from 21 patients were included. MR images were acquired using a 1.5T MRI scanner with
a spoiled 3D FLASH acquisition and a 4x2 circularly polarized phased-array neck coil. Four consecutive 3D images were acquired starting at approximately 3s after the administration of 0.1 mmoL/kg gadolinium. Subtraction images were generated to improve vessel-to-background contrast. To demonstrate the methods’ robustness against image noise, nine subtraction images were included. Curved multiplanar reformatted image slices were generated perpendicular to the vessel direction and an expert drew luminal contours on these slices. A fullwidth half-maximum criterion was applied to maintain the contour consistency, particularly for low vessel-tolumen contrast area. The user defined the artery section of interest by placing proximal and distal points. Subsequently, an
Figure 1 A segmentation result (blue surface). The middle figure is MIP image.
1 Dept. of Radiology, Leiden University Medical Center, Leiden, Netherlands Full list of author information is available at the end of the article
© 2011 van der Geest et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Suinesiaputra et al. Journal of Cardiovascular Magnetic Resonance 2011, 13(Suppl 1):P365 http://jcmr-online.com/content/13/S1/P365
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Figure 2 Comparisons between manual (M) and automated (A) in stenosed (SA), internal (ICA), external (ECA), common (CCA) and bifurcation (BA) sections.
automated detection of the vessel pathline was initiated. When the detection was not fully successful, additional intermediate points can be placed or curves can be drawn interactively to create forbidden planes. Subsequently, a tubular model was automatically defined along the pathline and an iterative fitting process was performed to move the control points to fit into the lumen based on the image gradient (Fig. 1 shows a segmentation result). Results of automated image segmentation were compared to the manually traced contours.
Figure 3 Stenosis grading a
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