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Abstract #3268

Machine Learning to Improve Compatibility of Multi Centre Arterial Spin Labelling Data to Characterise Dementia Spatial Perfusion Abnormality

Jack Highton1, Rebecca Steketee2, Rozanna Meijboom3, Marion Smits2, Ross Paterson4, Nick Fox4, Alexander Foulkes5, Catherine Slattery5, Jonathan Schott5, Enrico De Vita6, and David L Thomas7,8,9
1Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 2Department of Radiology and Nuclear Medicine, Erasmus Medical Centre, Rotterdam, Netherlands, Rotterdam, Netherlands, 3Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom, 4Institute of Neurology, University College London, London, United Kingdom, 5Dementia Research Centre, University College London, London, United Kingdom, 6King's College London, London, United Kingdom, 7University College London, London, United Kingdom, 8Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology,, University College London, London, United Kingdom, 9Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom

Arterial Spin Labelling (ASL) is a non-invasive MRI method to measure cerebral blood flow (CBF). Here, Support Vector Machine (SVM) based machine learning was used to identify spatial patterns of CBF abnormality in patients with Alzheimer’s Disease from two cohorts scanned at different centers. Support Vector Machine Regression models were found to be more accurate than conventional SVMs previously used for dementia classification. Then, motivated by the lack of ASL standardisation, an SVM based method was used to improve the compatibility of the two studies by removing differences in spatial patterns likely caused by differences in hardware and acquisition protocol.

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