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Abstract #0274

Dynamic 3D ASL in 20 Seconds Per Frame with Model-Based Image Reconstruction

Li Zhao 1 , Samuel W Fielden 2 , Xue Feng 2 , Max Wintermark 3 , John P Mugler III 4 , Josef Pfeuffer 5 , and Craig H Meyer 2,4

1 Radiology, Beth Israel Deaconess Medical Center & Harvard Medical School, Boston, MA, United States, 2 Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, 3 Radiology, Stanford University, Stanford, CA, United States, 4 Radiology, University of Virginia, Charlottesville, VA, United States, 5 Application Development, Siemens Healthcare, Erlangen, Germany

Dynamic arterial spin labeling (ASL) permits the tracking of a tagged blood bolus and reveals rich dynamic perfusion information. However, the inherent low SNR makes the acquisition of dynamic ASL data sets time-consuming and the resulting parameter maps unreliable. Using single-shot 3D stack-of-spirals acquisition and model-based image reconstruction, we demonstrate fast and robust dynamic ASL acquired in 20 seconds per perfusion phase, with high quality perfusion images and accurate parameter quantification.

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