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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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