Carotid intraplaque neovascularization quantification software (CINQS)
IEEE Journal of Biomedical and Health Informatics , Volume 19 - Issue 1 p. 332- 338
Intraplaque neovascularization (IPN) is an important biomarker of atherosclerotic plaque vulnerability. As IPN can be detected by contrast enhanced ultrasound (CEUS), imaging-biomarkers derived from CEUS may allow early prediction of plaque vulnerability. To select the best quantitative imaging-biomarkers for prediction of plaque vulnerability, a systematic analysis of IPN with existing and new analysis algorithms is necessary. Currently available commercial contrast quantification tools are not applicable for quantitative analysis of carotid IPN due to substantial motion of the carotid artery, artifacts, and intermittent perfusion of plaques. We therefore developed a specialized software package called Carotid intraplaque neovascularization quantification software (CINQS). It was designed for effective and systematic comparison of sets of quantitative imaging biomarkers. CINQS includes several analysis algorithms for carotid IPN quantification and overcomes the limitations of current contrast quantification tools and existing carotid IPN quantification approaches. CINQS has a modular design which allows integrating new analysis tools. Wizard-like analysis tools and its graphical-user-interface facilitate its usage. In this paper, we describe the concept, analysis tools, and performance of CINQS and present analysis results of 45 plaques of 23 patients. The results in 45 plaques showed excellent agreement with visual IPN scores for two quantitative imaging-biomarkers (The area under the receiver operating characteristic curve was 0.92 and 0.93).
|, , , , ,|
|IEEE Journal of Biomedical and Health Informatics|
|Organisation||Department of Biomedical Engineering|
Akkus, Z, van Burken, G, van den Oord, S.C.H, Schinkel, A.F.L, de Jong, N, van der Steen, A.F.W, & Bosch, J.G. (2015). Carotid intraplaque neovascularization quantification software (CINQS). IEEE Journal of Biomedical and Health Informatics, 19(1), 332–338. doi:10.1109/JBHI.2014.2306454