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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here