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Abstract #2820

Impact of ICA-based denoising of ASL data in clinical settings

Davide Carone1,2, George Harston1, Thomas Okell3, Michael Chappell3,4, and James Kennedy1

1Acute Vascular Imaging Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom, 2Laboratory of Experimental Stroke Research, Department of Surgery and Translational Medicine, Milan Center of Neuroscience, University of Milano Bicocca, Monza, Italy, 3Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 4Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom

ASL data has a low signal to noise ratio (SNR) and is sensitive to motion. Independent component analysis (ICA) has been successfully applied to denoise similar quality data in fMRI. We explored the effects of using an ICA approach on ASL data acquired in two different clinical settings. Mean cerebral blood flow (CBF) values were identical pre- and post- ICA indicating good signal preservation. However, the variance of CBF and bolus arrival time measures was significantly reduced suggesting a reduction in noise. These results suggest that ICA based denoising represents a promising strategy to improve ASL data quality.

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