Abstract #4526
Data driven feature learning for representation of myocardial BOLD MR Images
Anirban Mukhopadhyay 1 , Marco Bevilacqua 1 , Ilkay Oksuz 1 , Rohan Dharmakumar 2,3 , and Sotirios Tsaftaris 1,4
1
IMT Institute for Advanced Studies Lucca,
Lucca, LU, Italy,
2
Biomedical
Imaging Research Institute, Cedars-Sinai Medical Center,
Los Angeles, CA, United States,
3
Medicine,
University of California, Los Angeles, Los Angeles, CA,
United States,
4
Electrical
Engineering and Computer Science, Northwestern
University, Evanston, Il, United States
Cardiac phase-dependent variations of myocardial signal
intensities in Cardiac Phase-resolved
Blood-Oxygen-Level-Dependent (CP-BOLD) MRI can be
exploited for the identification of ischemic
territories. This technique requires segmentation to
isolate the myocardium. However, spatio-temporal
variations of BOLD contrast, prove challenging for
existing automated myocardial segmentation techniques,
because they were developed for acquisitions where
contrast variations in the myocardium are minimal.
Appropriate feature learning mechanisms are necessary to
best represent appearance and texture in CP-BOLD data.
Here we propose and validate a feature learning
technique based on multiscale dictionary model that
learns to sparsely represent effective patterns under
healthy and ischemic conditions.
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