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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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