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Abstract #3694

Stochastic variational inference improves arterial transit time estimation from multi-delay arterial spin labelling data

Thomas Kirk1, Georgia Kenyon2, Martin Craig1, and Michael Chappell1,3
1Quantified Imaging Limited, London, United Kingdom, 2University of Adelaide, Adelaide, Australia, 3University of Nottingham, Nottingham, United Kingdom

Synopsis

Keywords: Arterial Spin Labelling, Perfusion, arterial transit time

Motivation: Arterial transit time (ATT) is a quantity of increasing clinical interest for understanding neurovascular health. ATT may be measured using multi-delay arterial spin labelling (ASL), but due to the low signal to noise ratio and non-linear kinetics of this imaging technique, obtaining accurate and robust measurements can be challenging.

Goal(s): To better estimate arterial transit time from multiple-delay arterial spin labelling data.

Approach: Model-fitting via stochastic variational Bayesian inference with joint spatial and non-spatial prior regularisation.

Results: The new method is more robust than existing methods on noisy data, and produces maps with greater anatomical detail on acquisition data.

Impact: The advantages of this new method will enable researchers to better exploit ATT measurement via multi-delay ASL, furthering understanding of neurovascular health.

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