Abstract #4527
Dictionary-based Support Vector Machines for Unsupervised Ischemia Detection at Rest with CP-BOLD Cardiac MRI
Marco Bevilacqua 1 , Anirban Mukhopadhyay 1 , Ilkay Oksuz 1 , Cristian Rusu 2 , Rohan Dharmakumar 3,4 , and Sotirios A. Tsaftaris 1,5
1
IMT Institute for Advanced Studies, Lucca,
LU, Italy,
2
University
of Vigo, Vigo, Galicia, Spain,
3
Biomedical
Imaging Research Institute, Cedars-Sinai Medical Center,
Los Angeles, CA, United States,
4
Medicine,
University of California, Los Angeles, CA, United
States,
5
Electrical
Engineering and Computer Science, Northwestern
University, Evanston, IL, United States
Cardiac Phase-resolved Blood-Oxygen-Level-Dependent
(CP-BOLD) MRI has been recently demonstrated to detect
an ongoing myocardial ischemia at rest, taking advantage
of spatio-temporal patterns in myocardial signal
intensities, which are modulated by the presence of
disease. However, this approach does require significant
post-processing to detect the disease and to this day
only a few images of the acquisition are used coupled
with fixed thresholds to establish biomarkers. We
propose a threshold-free unsupervised approach, based on
dictionary learning and one-class support vector
machines, which can generate a probabilistic ischemia
likelihood map.
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