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Abstract #3270

Voxel-wise DCE-MRI model selection impacts estimates of blood-brain barrier leakage in stroke

Olivia A Jones1,2, Ben R Dickie2,3, Adrian R Parry-Jones2,4,5, David Higgins6, Hamied A Haroon1,2, Sarah Al-Bachari7, Hedley Emsley8,9, and Laura M Parkes1,2
1Division of Psychology, Communication and Human Neuroscience, University of Manchester, Manchester, United Kingdom, 2Geoffrey Jefferson Brain Research Centre, Manchester, United Kingdom, 3Division of Informatics, Imaging and Data Science, University of Manchester, Manchester, United Kingdom, 4Division of Cardiovascular Science, University of Manchester, Manchester, United Kingdom, 5Manchester Centre for Clinical Neurosciences, Northern Care Alliance NHS Foundation Trust, Manchester, United Kingdom, 6Philips, Farnborough, United Kingdom, 7University College London, London, United Kingdom, 8Lancaster Medical School, Lancaster University, Lancaster, United Kingdom, 9Department of Neurology, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, United Kingdom

Synopsis

Keywords: Data Processing, DSC & DCE Perfusion, Blood-brain barrier; Biology, methods and models; Modelling; Permeability

Motivation: Different models of DCE-MRI tracer leakage may fit better in different brain regions in cerebrovascular disease and could provide additional insight into BBB function.

Goal(s): Demonstrate the utility of voxel-wise DCE-MRI model selection in stroke.

Approach: Fit the Extended Tofts, Patlak, and Intravascular models of DCE-MRI tracer leakage to data from controls, ischaemic stroke, and intracerebral haemorrhage patients on a voxel-wise basis, and select the best-fitting model for each voxel using the Akaike Information Criterion.

Results: Different models are preferred in different tissue types and disease groups. Model selection increases inter-patient variance of Ktrans compared to Patlak alone.

Impact: The Patlak model may not be the most appropriate model for DCE-MRI measurements of blood-brain barrier leakage in ischaemic stroke. Best-fitting model maps could help delineate the extent of “leaky” vs “non leaky” regions based on nested model assumptions.

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Keywords