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Abstract #0415

Multi-Resolution Registration and Segmentation for cardiac BOLD MRI

Ilkay Oksuz1,2, Rohan Dharmakumar3,4, and Sotirios A. Tsaftaris2,5

1Diagnostic Radiology, Yale University, New Haven, CT, United States, 2IMT Institute for Advanced Studies Lucca, Lucca, Italy, 3Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, United States, 4University of California, Los Angeles, CA, United States, 5The University of Edinburgh, Edinburgh, United Kingdom

Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP-BOLD) MRI is a new contrast and stress-free approach for detecting myocardial ischemia, that identifies the ischemic myocardium by examining changes in myocardial signal intensity patterns as a function of cardiac phase. But, these changes coupled with cardiac motion, challenge automated standard CINE MR myocardial segmentation and registration techniques resulting in a significant drop of segmentation and registration accuracy. We propose a dictionary learning based multi-resolution registration scheme for supervised learning and sparse representation of the myocardium. Our results show an improvement of 15% myocardial segmentation w.r.t. the state of the art.

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