Abstract #3269

Untangling Perfusion from Atrophy in Fronto-Temporal Dementia using Multimodal Correlation and Machine Learning

Jack Highton¹, Rebecca Steketee², Rozanna Meijboom³, Marion Smits², Enrico De Vita⁴, Jonathan Schott⁵, and David L Thomas^6,7,8

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, ⁴King's College London, London, United Kingdom, ⁵University College London, London, United Kingdom, ⁶Dementia Research Centre, University College London, London, United Kingdom, ⁷Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, United Kingdom, ⁸Wellcome Centre for Human Neuroimaging, Institute of Neurology,, University College London, London, United Kingdom

Partial volume correction (PVC) is often applied to analyse cerebral blood flow (CBF) in dementia, in order to isolate perfusion changes from atrophy of grey matter. But PVC inevitably results in spatial smoothing of CBF, which may weaken the informative spatial pattern of CBF changes.

Here, machine learning was used to detect Fronto-Temporal Dementia (FTD), considering spatial patterns in CBF from Arterial Spin Labelling, with and without PVC. Also, multimodal voxel concordance analysis untangled the spatial relationship between GM atrophy and CBF. This information was used with CBF PVC to improve the FTD detection accuracy without the atrophy signature.

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