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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