Print Email Facebook Twitter Plaque characterization in ex vivo MRI evaluated by dense 3D correspondence with histology Title Plaque characterization in ex vivo MRI evaluated by dense 3D correspondence with histology Author Van Engelen, A. De Bruijne, M. Klein, S. Verhagen, H. Groen, H. Wentzel, J. Van der Ligt, A. Niessen, W. Faculty Applied Sciences Department Imaging Science and Technology Date 2011-03-08 Abstract Automatic quantification of carotid artery plaque composition is important in the development of methods that distinguish vulnerable from stable plaques. MRI has shown to be capable of imaging different components noninvasively. We present a new plaque classification method which uses 3D registration of histology data with ex vivo MRI data, using non-rigid registration, both for training and evaluation. This is more objective than previously presented methods, as it eliminates selection bias that is introduced when 2D MRI slices are manually matched to histological slices before evaluation. Histological slices of human atherosclerotic plaques were manually segmented into necrotic core, fibrous tissue and calcification. Classification of these three components was voxelwise evaluated. As features the intensity, gradient magnitude and Laplacian in four MRI sequences after different degrees of Gaussian smoothing, and the distances to the lumen and the outer vessel wall, were used. Performance of linear and quadratic discriminant classifiers for different combinations of features was evaluated. Best accuracy (72.5 ± 7.7%) was reached with the linear classifier when all features were used. Although this was only a minor improvement to the accuracy of a classifier that only included the intensities and distance features (71.6 ± 7.9%), the difference was statistically significant (paired t-test, p<0.05). Good sensitivity and specificity for calcification was reached (83% and 95% respectively), however, differentiation between fibrous (sensitivity 85%, specificity 60%) and necrotic tissue (sensitivity 49%, specificity 89%) was more difficult. Subject atherosclerosissegmentationquantificationpattern recognition To reference this document use: http://resolver.tudelft.nl/uuid:13a2ed14-ae75-436c-883b-11d9b5f015cb DOI https://doi.org/10.1117/12.878007 Publisher SPIE ISSN 0277-386X Source https://doi.org/10.1117/12.878007 Source Proceedings of SPIE, 2011 vol. 7963 Part of collection Institutional Repository Document type conference paper Rights (c)2011 The Authors Files PDF 2011-7963.Engelen.pdf 1.7 MB Close viewer /islandora/object/uuid:13a2ed14-ae75-436c-883b-11d9b5f015cb/datastream/OBJ/view