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Abstract #1314

Automatic Respiratory Self-Navigation Processing (ASAP) for Coronary MRA Using Principal Component Analysis

Jianing Pang1, 2, Hsin-Jung Yang1, 3, Yibin Xie1, 3, Rohan Dharmakumar1, Debiao Li1, 3

1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States; 2Biomedical Engineering, Northwestern University, Chicago, IL, United States; 3Bioengineering, University of California, Los Angeles, CA, United States

In this work we propose an automatic processing strategy for respiratory self-navigation based on principal component analysis. Numerical simulation and in vivo study were performed. Results show that the proposed method accurately captures the simulated motion, and when applied to the in vivo coronary MRA dataset one is able to reduce motion blurring from phase correction using the detected translational motion.