Abstract #5081

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

David Owen¹, Andrew Melbourne², David L Thomas^2,3, 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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