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Abstract #1659

Automated classification of vessel disease based on high-resolution intravascular multi-parametric mapping MRI

Guan Wang 1,2 , M. Arcan Erturk 3 , Shashank Sathyanarayana Hegde 2 , and Paul A. Bottomley 1,2

1 Dept. of Electrical & Computer Engineering, Johns Hopkins University, Baltimore, MD, United States, 2 Russell H. Morgan Dept. of Radiology & Radiological Sciences, Johns Hopkins University, Baltimore, MD, United States, 3 Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States

The ability to characterize atheroma components is central to assessing the status of vessel disease, its progression and response to interventions, diet, etc, but is poorly served by existing modalities. To test whether high-field, high-resolution intravascular MRI (IVMRI) combined with quantitative T1, T2, proton density and mobile lipid mapping could be used to stage vessel disease, we applied 200m resolution multi-parametric 3T MRI to diseased human artery specimens. The results were used to train an automatic machine-learning-based classifier to classify disease, and the performance was compared with histology.

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