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Abstract #5081

ADRIMO: Anatomy-DRIven MOdelling of spatial correlation to improve analysis of arterial spin labelling data

David Owen1, Andrew Melbourne2, David L Thomas2,3, Joanne Beckmann4, Jonathan Rohrer3, Neil Marlow4, and Sebastien Ourselin2

1Translational Imaging Group, University College London, London, United Kingdom, 2Translational Imaging Group, University College London, 3Dementia Research Centre, University College London, 4Institute for Women's Health, University College London

Arterial spin labelling (ASL) offers valuable measurements of perfusion in the brain and other organs. However, ASL data have low SNR and are prone to partial volume effects. We present a Bayesian model of anatomically-derived spatial correlation in ASL data (ADRIMO), which improves the accuracy of perfusion estimates and hence improves the analysis of ASL data. The method is assessed experimentally by examining ASL images from a cohort of 130 preterm-born adolescents.

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