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Abstract #0732

Motion-Robust ASL Perfusion Imaging with Self-Navigation Using 3D GRASE and CAIPI Sampling

Minhao Hu1, Mark Chiew2,3, Frederik Lange1, Peter Jezzard1, Joseph G. Woods1, and Thomas W. Okell1
1Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 2Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada, 3Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada

Synopsis

Keywords: Arterial Spin Labelling, Arterial spin labelling

Motivation: Perfusion imaging using Arterial Spin Labeling (ASL) relies on accurate alignment between tag and control images, making it particularly vulnerable to head motion.

Goal(s): To correct the inter-shot motion, minimize motion-induced artefacts and enhance perfusion image quality without increasing scan time.

Approach: Using pCASL with a segmented 3D GRASE readout and CAIPI sampling, we employ a 3D self-navigator to estimate inter-shot motion. Motion-corrected images are generated through motion-compensated SENSE reconstruction.

Results: Motion-corrected perfusion images show reduced artifacts, higher temporal SNR within gray matter, and improved correlation and SSIM between motion-free and motion-corrected images.

Impact: We investigate a motion-robust pCASL sequence using a segmented 3D GRASE readout with CAIPI sampling, improving perfusion image quality in the presence of inter-shot motion without increasing scan time. This approach facilitates future clinical applicationwith less cooperative patients.

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Keywords