MRI-based parameter inference for cerebral perfusion modelling in health and ischaemic stroke

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dc.contributor.authorJózsa, Tamás István
dc.contributor.authorPetr, J.
dc.contributor.authorPayne, S. J.
dc.contributor.authorMutsaerts, H. J. M. M.
dc.date.accessioned2023-10-24T12:17:22Z
dc.date.available2023-10-24T12:17:22Z
dc.date.issued2023-10-12
dc.description.abstractCerebral perfusion modelling is a promising tool to predict the impact of acute ischaemic stroke treatments on the spatial distribution of cerebral blood flow (CBF) in the human brain. To estimate treatment efficacy based on CBF, perfusion simulations need to become suitable for group-level investigations and thus account for physiological variability between individuals. However, computational perfusion modelling to date has been restricted to a few patient-specific cases. This study set out to establish automated parameter inference for perfusion modelling based on neuroimaging data and thus enable CBF simulations of groups. Magnetic resonance imaging (MRI) data from 75 healthy senior adults were utilised. Brain geometries were computed from healthy reference subjects’ T1-weighted MRI. Haemodynamic model parameters were determined from spatial CBF maps measured by arterial spin labelling (ASL) perfusion MRI. Thereafter, perfusion simulations were conducted in 75 healthy cases followed by 150 acute ischaemic stroke cases representing an occlusion and CBF cessation in the left and right middle cerebral arteries. The anatomical fitness of the brain geometries was evaluated by comparing the simulated grey (GM) and white matter (WM) volumes to measurements in healthy reference subjects. Strong positive correlations were found in both tissue types (GM: Pearson’s r 0.74, P<0.001; WM: Pearson’s r 0.84, P<0.001). Haemodynamic parameter tuning was verified by comparing the total volumetric blood flow rate to the brain in healthy reference subjects and simulations (Pearson’s r 0.89, P<0.001). In acute ischaemic stroke cases, the simulated infarct volume using a perfusion-based estimate was 197±25 ml. Computational predictions were in agreement with anatomical and haemodynamic values from the literature concerning T1-weighted, T2-weighted, and phase-contrast MRI measurements in healthy scenarios and acute ischaemic stroke cases. The acute stroke simulations did not capture small infarcts (left tail of the distribution), which could be explained by neglected compensatory mechanisms, e.g. collaterals. The proposed parameter inference method provides a foundation for group-level CBF simulations and for in silico clinical stroke trials which could assist in medical device and drug development.en_UK
dc.description.sponsorshipEuropean Union funding: 777072en_UK
dc.identifier.citationJózsa TI, Petr J, Payne SJ, Mutsaerts HJMM. (2023) MRI-based parameter inference for cerebral perfusion modelling in health and ischaemic stroke, Computers in Biology and Medicine, Volume 166, November 2023, Article Number 107543en_UK
dc.identifier.eissn1879-0534
dc.identifier.issn0010-4825
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2023.107543
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20432
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectCerebral blood flowen_UK
dc.subjectAcute ischaemic strokeen_UK
dc.subjectMagnetic resonance imagingen_UK
dc.subjectComputational modellingen_UK
dc.titleMRI-based parameter inference for cerebral perfusion modelling in health and ischaemic strokeen_UK
dc.typeArticleen_UK

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