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Abstract #2391

A Synthetic Generator of Myocardial Blood-Oxygen-Level-Dependent MRI Timeseries with Structural Sparse Decomposition Modeling

Cristian Rusu 1 , Rohan Dharmakumar 2,3 , and Sotirios A. Tsaftaris 1,4

1 IMT Institute for Advanced Studies Lucca, Lucca, Italy, 2 Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 3 Medicine, University of California, Los Angeles, CA, United States, 4 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 for the identification of ischemic territories under resting conditions. Lack of accurate registration, necessary to provide pixel-to-pixel correspondences in the cardiac cycle, causes the majority of analysis to rely on segmental definitions of the myocardium and to use a few cardiac phases, decreasing the potential diagnostic power of the technique. To accelerate the development of methods that could potentially yield pixel-level characterization of ischemia, we propose and validate a synthetic CP-BOLD timeseries generator based on a composite dictionary model that learns to represent efficiently patterns under healthy and ischemic conditions.

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