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Abstract #2948

Accelerating Dual Venc 4D Flow Using Compressed Sensing with Locally Low Rank along Velocity Encoding

Peng Lai1, Fatih Suleyman Hafalir2, Joseph Y Cheng3, Jonathan I Tamir4, Shreyas S Vasanawala3, Anja C.S Brau1, and Martin A Janich5

1Global MR Applications and Workflow, GE Healthcare, Menlo Park, CA, United States, 2Technical University of Munich, Munich, Germany, 3Radiology, Stanford University, Stanford, CA, United States, 4Electrical Engineering and Computer Science, University of California, Berkeley, CA, United States, 5Global MR Applications and Workflow, GE Healthcare, Munich, Germany

Dual Venc has been developed to improve the accuracy of conventional 4D flow in high dynamic range of velocity. However, dual Venc acquisition doubles scan time. This work explored a high dimensional compressed sensing method to accelerate dual Venc 4D flow by utilizing additional data redundancy in the velocity encoding dimension.

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