Spatio-temporal texture (SpTeT) for distinguishing vulnerable from stable atherosclerotic plaque on dynamic contrast enhancement (DCE) MRI in a rabbit model.
OBJECTIVE
To develop a new spatio-temporal texture (SpTeT) based method for distinguishing vulnerable versus stable atherosclerotic plaques on DCE-MRI using a rabbit model of atherothrombosis.


METHODS
Aortic atherosclerosis was induced in 20 New Zealand White rabbits by cholesterol diet and endothelial denudation. MRI was performed before (pretrigger) and after (posttrigger) inducing plaque disruption with Russell's-viper-venom and histamine. Of the 30 vascular targets (segments) under histology analysis, 16 contained thrombus (vulnerable) and 14 did not (stable). A total of 352 voxel-wise computerized SpTeT features, including 192 Gabor, 36 Kirsch, 12 Sobel, 52 Haralick, and 60 first-order textural features, were extracted on DCE-MRI to capture subtle texture changes in the plaques over the course of contrast uptake. Different combinations of SpTeT feature sets, in which the features were ranked by a minimum-redundancy-maximum-relevance feature selection technique, were evaluated via a random forest classifier. A 500 iterative 2-fold cross validation was performed for discriminating the vulnerable atherosclerotic plaque and stable atherosclerotic plaque on per voxel basis. Four quantitative metrics were utilized to measure the classification results in separating between vulnerable and stable plaques.


RESULTS
The quantitative results show that the combination of five classes of SpTeT features can distinguish between vulnerable (disrupted plaques with an overlying thrombus) and stable plaques with the best AUC values of 0.9631 ± 0.0088, accuracy of 89.98% ± 0.57%, sensitivity of 83.71% ± 1.71%, and specificity of 94.55% ± 0.48%.


CONCLUSIONS
Vulnerable and stable plaque can be distinguished by SpTeT based features. The SpTeT features, following validation on larger datasets, could be established as effective and reliable imaging biomarkers for noninvasively assessing atherosclerotic risk.
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106.
Tao
T
Wan
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106.
Anant
A
Madabhushi
Division of Imaging Sciences and Biomedical Engineering, King's College London, London SE1 7EH, United Kingdom.
Alkystis
A
Phinikaridou
Department of Physiology and Biophysics, Boston University School of Medicine, Boston, Massachusetts 02118.
James A
JA
Hamilton
Department of Physiology and Biophysics, Boston University School of Medicine, Boston, Massachusetts 02118.
Ning
N
Hua
Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215.
Tuan
T
Pham
Elucid Bioimaging Inc., Wenham, Massachusetts 01984.
Jovanna
J
Danagoulian
Elucid Bioimaging Inc., Wenham, Massachusetts 01984.
Ross
R
Kleiman
Elucid Bioimaging Inc., Wenham, Massachusetts 01984.
Andrew J
AJ
Buckler
D000818:Animals; D001158:Arteries / Q000473:pathology; D004195:Disease Models, Animal; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D058226:Plaque, Atherosclerotic / Q000150:complications* / Q000175:diagnosis*; D011817:Rabbits; D062211:Spatio-Temporal Analysis; D013927:Thrombosis / Q000150:complications*