Abstract #2704
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
1
Department of Neurological and Movement
Sciences, University of Verona, Verona, Italy,
2
Institute
of Biomedical Engineering, University of Oxford, Oxford,
United Kingdom,
3
Academic
Neuroradiological Unit, Department of Brain Repair and
Rehabilitation, UCL Institute of Neurology, London,
United Kingdom,
4
Lysholm
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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