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Abstract #0422

A Bayesian Approach to Enable Single Breath-Hold 4D Flow MRI

Adam Rich1, Lee C. Potter1,2, Ning Jin3, Yingmin Liu2, Orlando P. Simonetti2,4,5, and Rizwan Ahmad1,2

1Electrical and Computer Engineering, The Ohio State University, Columbus, OH, United States, 2Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH, United States, 3Siemens Medical Solutions, Columbus, OH, United States, 4Department of Radiology, Columbus, OH, United States, 5Division of Cardiovascular Medicine Department of Internal Medicine, Columbus, OH, United States

PC-MRI based 4D flow imaging is a powerful tool to quantify hemodynamics within the heart and the great vessels. We develop a Bayesian technique to greatly accelerate 4D flow image acquisition. Our technique exploits the rich structure in 4D flow images within a joint reconstruction algorithm. We validate the technique using retrospectively accelerated flow phantom data and prospectively accelerated, single breath-hold in vivo data. In the flow phantom, stroke volume differed by ≤9% for R≤28 when compared to fully sampled data. The high acceleration provided by the Bayesian approach could allow for clinical application of single breath-hold 4D flow imaging.

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