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Abstract #0706

Motion-robust pediatric abdominal angiography using dynamic 3D image self-navigator with 3D cones

Srivathsan P. Koundinyan1, Frank Ong2, Dwight G. Nishimura1, Shreyas S. Vasanawala3, and Joseph Y. Cheng3

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, United States, 3Radiology, Stanford University, Stanford, CA, United States

Respiratory motion detection using the k-space center (DC signal) can be problematic in pediatric subjects exhibiting highly irregular breathing patterns. To address this problem, we extract respiratory motion information from high frame rate (3.3 Hz), low-resolution (7.7 mm) 3D self-navigators derived from the imaging data (i.e., central portion of 3D cones k-space data) and reconstructed with a multi-scale low-rank framework. Localized motion estimates are obtained from the 3D self-navigators using optical flow registration. We demonstrate that region-specific motion information from 3D self-navigators better mitigates motion artifacts compared to the low-pass filtered DC signal in pediatric abdominal angiography exams.

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