Abstract #5022

A Pipeline for ASL Quantification and Analysis using Inter-regional Differences and Machine Learning: Application to Young Onset Alzheimer’s Disease

Jack Highton¹, Enrico De Vita², Jonathan Schott³, and David L Thomas^4,5

¹Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, ²Department of Biomedical Engineering, King's College London, London, United Kingdom, ³Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, ⁴Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, ⁵Leonard Wolfson Experimental Neurology Centre, University College London, London, United Kingdom

Arterial Spin Labelling (ASL) is an MRI method to measure cerebral blood flow with potential to assist early dementia diagnosis. Here, ASL data acquired from patients with young onset Alzheimer’s disease (AD) was analysed, using both a novel region based statistical approach and voxel based machine learning. This is the first study to analyse ASL data from patients with Posterior Cortical Atrophy using machine learning. Both approaches are shown to identify regions known to be affected by AD. Inter-region analysis suggests the parietal lobe is the most useful benchmark region, to separate region specific hypoperfusion from global perfusion changes.

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