Abstract
The analysis of plaque deposits in the coronary vasculature is an important topic in current clinical research. From a technical side mostly new algorithms for different sub tasks e.g. centerline extraction or vessel/plaque segmentation – are proposed. However, to enable clinical research with the help of these algorithms, a software solution, which enables manual correction, comprehensive visual feedback and tissue analysis capabilities, is needed. Therefore, we want to present such an integrated software solution. A MeVisLab-based implementation of our solution is available as part of the Siemens Healthineers syngo.via Frontier and OpenApps research extension. It is able to perform robust automatic centerline extraction and inner and outer vessel wall segmentation, while providing easy to use manual correction tools. Also, it allows for annotation of lesions along the centerlines, which can be further analyzed regarding their tissue composition. Furthermore, it enables research in upcoming technologies and research directions: it does support dual energy CT scans with dedicated plaque analysis and the quantification of the fatty tissue surrounding the vasculature, also in automated set-ups.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Mendis S, Davis S, Norrving B. Organizational update: the World health organization global status report on noncommunicable diseases 2014. Stroke. 2015;46(5):e121–e122.
Naghavi M. From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies. Part II. Circulation. 2003;108:1772–1778.
Antonopoulos AS, et al. Detecting human coronary inflammation by imaging perivascular fat. Sci Transl Med. 2017;9(398).
Zheng Y, Barbu A, Georgescu B, et al. Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans Med Imaging. 2008;27(11):1668–1681.
Zheng Y, Tek H, Funka-Lea G. Robust and accurate coronary artery centerline extraction in CTA by combining model-driven and data-driven approaches. Proc MICCAI. 2013; p. 74–81.
Schaap M, Metz CT, van Walsum T, et al. Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Med Image Anal. 2009;13(5):701–714.
Lugauer F, Zheng Y, Hornegger J, et al. Precise lumen segmentation in coronary computed tomography angiography. Proc MICCAI. 2014; p. 137–147.
Kirişli H, et al. Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography. Med Image Anal. 2013;17(8):859–876.
Grosskopf S, Biermann C, Deng K, et al. Accurate, fast, and robust vessel contour segmentation of CTA using an adaptive self-learning edge model. In: Medical Imaging 2009: Image Processing. vol. 7259. International Society for Optics and Photonics; 2009. p. 72594D.
Wels M, Lades F, Hopfgartner C, et al. Intuitive and accurate patient-specific coronary tree modeling from cardiac computed-tomography angiography. In: The 3rd interactive MIC Workshop; 2016. p. 86-93.
Danad I, Ó Hartaigh B, Min JK. Dual-energy computed tomography for detection of coronary artery disease. Expert Rev Cardiovasc Ther. 2015;13(12):1345–1356.
BarretoM, Schoenhagen P, Nair A, et al. Potential of dual-energy computed tomography to characterize atherosclerotic plaque: ex vivo assessment of human coronary arteries in comparison to histology. J Cardiovasc Comput Tomogr. 2008;2(4):234–242.
Tesche C, et al. Coronary CT angiography derived morphological and functional quantitative plaque markers correlated with invasive fractional flow reserve for detecting hemodynamically significant stenosis. J Cardiovasc Comput Tomogr. 2016;10(3):199–206.
Ratiu M, et al. Impact of coronary plaque geometry on plaque vulnerability and its association with the risk of future cardiovascular events in patients with chest pain undergoing coronary computed tomographic angiography: the GEOMETRY study: Protocol for a prospective clinical trial. Medicine. 2018;97(49).
Morariu M, et al. Impact of inflammation-mediated response on pan-coronary plaque vulnerability, myocardial viability and ventricular remodeling in the postinfarction period-the VIABILITY study: Protocol for a non-randomized prospective clinical study. Medicine. 2019;98(17).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Der/die Autor(en), exklusiv lizenziert durch Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Denzinger, F. et al. (2021). Coronary Plaque Analysis for CT Angiography Clinical Research. In: Palm, C., Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2021. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_53
Download citation
DOI: https://doi.org/10.1007/978-3-658-33198-6_53
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-33197-9
Online ISBN: 978-3-658-33198-6
eBook Packages: Computer Science and Engineering (German Language)