Reducing blurring artifacts in 3D-GRASE ASL by integrating new acquisition and analysis strategies
Ilaria Boscolo Galazzo 1 , Michael A Chappell 2 , David L Thomas 3 , Xavier Golay 3 , Paolo Manganotti 1 , and Enrico De Vita 3,4
Department of Neurological and Movement
Sciences, University of Verona, Verona, Italy,
of Biomedical Engineering, University of Oxford, Oxford,
Neuroradiological Unit, Department of Brain Repair and
Rehabilitation, UCL Institute of Neurology, London,
Department of Neuroradiology, National Hospital for
Neurology and Neurosurgery, London, United Kingdom
3D-GRASE is one of the most efficient readout schemes
for Arterial Spin Labeling (ASL) when whole brain
coverage is desired. Due to the length of the echo
train, single-shot 3D-GRASE images exhibit severe T2
blurring along the partition-encoding direction. We
present a procedure to reduce the blurring effect in
single inversion-time data, combining a multi-shot
3D-GRASE-ASL sequence with a post-acquisition deblurring
algorithm. The application of this algorithm allows a
reduction of the number of shots needed for each image.
As more averages can be collected for a fixed
acquisition time, this method improves the available
signal-to-noise ratio and data quality.
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