NDLI: Analysis of neovascularization of atherosclerotic carotid plaques in contrast enhanced ultrasound

Content Provider	IEEE Xplore Digital Library
Author	Akkus, Z. Hoogi, A. Bosch, J.G. Renaud, G. van den Oord, S.C.H. ten Kate, G.L. Schinkel, A.F.L. Adam, D. de Jong, N. van der Steen, A.F.W.
Copyright Year	2012
Description	Author affiliation: Biomed. Eng., Technion - Israel Inst. of Technol., Haifa, Israel (Adam, D.) \|\| Thoraxcenter Biomed. Eng., Erasmus MC, Rotterdam, Netherlands (Akkus, Z.; Hoogi, A.; Bosch, J.G.; Renaud, G.; de Jong, N.; van der Steen, A.F.W.) \|\| Dept. of Cardiology, Erasmus MC, Rotterdam, Netherlands (van den Oord, S.C.H.; ten Kate, G.L.; Schinkel, A.F.L.)
Abstract	Intraplaque neovascularization (IPN) is linked to progressive atherosclerotic disease and plaque instability. Contrast enhanced ultrasound (CEUS) can detect these microvessels. Quantification of IPN may allow early detection of vulnerable plaques. We developed a semiautomatic quantification of IPN in CEUS, with motion compensation, contrast spot detection, tracking and classification, and vascular tree reconstruction. Side-by-side CEUS and B-mode carotid images were analyzed (Philips iU22, L9-3 linear array). The plaque motion pattern was obtained from B-mode with block matching (BM) and multidimensional dynamic programming (MDP) and applied to CEUS images for motion correction. In BM, a 6×4mm fixed template was scanned over a 6×2mm search region and normalized correlation coefficients were used in MDP to find the optimal 2D displacement path over time. Image sequences were divided into sets of 10 frames with 80% overlap. In frame 1 of each set, artificial bubble templates detect contrast bubbles within plaque. Templates of 1.3×1.3mm around detected objects were tracked over 10 frames using BM and MDP. Tracks were classified as moving bubbles or artifacts based on their motion. From the overlapping sets, tracks were merged and vessel paths quantified. Automated detection/ tracking / grading of IPN were validated against manual tracking and visual grading by two physicians in 28 plaques. Our algorithm detected 101 of 104 visually identified contrast spots. In 90 of 101 objects (89%), mean error between automated and manual tracking was <; 0.5mm. 81 detected objects (78%) were correctly classified into artifacts and microvessels. Two physicians independently scored plaques into 4 grades of IPN. Automated IPN score was identical to visual scoring in 64%, 1 grade difference in 27% and 2 grades in 9%, which is very comparable to the interobserver differences (68%, 25%, 7%). Our algorithm can successfully detect and track contrast bubbles, classify objects into microvessels and artifacts, and reconstruct microvessel trees. The automated IPN score is equivalent to an expert visual score.
Starting Page	918
Ending Page	921
File Size	864312
Page Count	4
File Format	PDF
ISBN	9781467345613
ISSN	19485719
e-ISBN	9781467345620
DOI	10.1109/ULTSYM.2012.0229
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-07
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Tracking Ultrasonic imaging Medical services Dynamic programming Motion compensation Classification algorithms contrast agents contrast enhanced ultrasound microbubble detection microbubble tracking vascular tree reconstruction motion compensation
Content Type	Text
Resource Type	Article

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