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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