Accelerated dynamic Fourier velocity encoding by exploiting velocity-spatio-temporal correlations.
OBJECTIVE
To describe how the information content in a Fourier velocity encoding (FVE) scan can be transformed into a very sparse representation and to develop a method that exploits the compactness of the data to significantly accelerate the acquisition.


METHODS
For validation, fully sampled FVE datasets were acquired in phantom and in vivo experiments. Fivefold and eightfold acceleration was simulated by using only one fifth or one eighth of the data for reconstruction in the proposed method based on the k-t BLAST framework. Reconstructed images were compared quantitatively to those from the fully sampled data.


RESULTS
Velocity spectra in the accelerated datasets were comparable to the spectra from fully sampled datasets. The detected peak velocities remained accurate even at eightfold acceleration, and the overall shape of the spectra was well preserved. Slight temporal smoothing was seen in the accelerated datasets.


CONCLUSIONS
A novel technique for accelerating time-resolved FVE scan is presented. It is possible to accelerate FVE to acquisition speeds comparable to a standard time-resolved phase-contrast scan.
Hansen
Michael S
MS
Institute for Biomedical Engineering, Swiss Federal Institute of Technology (ETH), University of Zurich, Zurich, Switzerland. msh@mr.au.dk
Baltes
Christof
C
Tsao
Jeffrey
J
Kozerke
Sebastian
S
Pruessmann
Klaas P
KP
Boesiger
Peter
P
Pedersen
Erik M
EM
D000465:Algorithms; D001185:Artificial Intelligence; D001783:Blood Flow Velocity / Q000502:physiology*; D001921:Brain / Q000098:blood supply* / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D002560:Cerebrovascular Circulation; D005583:Fourier Analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D019047:Phantoms, Imaging; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic