Modelling the leptomeningeal collateral circulation during acute ischaemic stroke

Padmos, Raymond M.; Terreros, Nerea Arrarte; Józsa, Tamás; Závodszky, Gábor; Marquering, Henk A.; Majoie, Charles B. L. M.; Hoekstra, Alfons G.

doi:10.1016/j.medengphy.2021.03.003

Cited by 14 publications

(21 citation statements)

References 45 publications

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“…This is consistent with theories on the physiology of LMAs 9 , 20 , 36 and findings from computational studies. 25 , 27 , 28 Our baseline model shows values of FD and PI well aligned with the literature. 17 , 33 Flow velocity increases in ipsilateral ACA and PCA because of the negligible variations in their radius, combined with the reduction of distal resistance caused by the recruitment of LMAs.…”

Section: Discussionsupporting

confidence: 78%

“…study, Padmos and colleagues showed that increasing the number of LMAs strengthens the connection between different brain regions and has the overall effect of reducing the resistance between the regions downstream the occlusion, improving exponentially the transport properties of a contrast medium across an occlusion. 25 This is equivalent to the reduction in resistance observed in our model when the LMAs are widened.…”

Section: Discussionmentioning

confidence: 67%

“…Another limitation is the extension of the network: existing studies in the literature have adopted representations of the brain circulation with thousands of vessels but limited their analysis to steady scenarios. 25 In our case the limited extension of the network limited the computational effort and allowed a transient description of the cardiac cycle. This led to the identification of biomarkers associated to maximum, average and minimum values of the velocities, not possible with steady models.…”

Section: Discussionmentioning

confidence: 99%

“…1D models allow either steady or transient simulations of larger vascular networks in shorter time frames, 30 can incorporate nonlinear material properties, 5 and have been used to study the brain circulation in physiological 16 , 44 and pathological conditions, both alone 1 , 32 or integrated with data driven techniques. 24 , 40 Only recently have researchers started to include the pial circulation into their models, 25 , 27 , 28 rarely proposing methodologies for clinical exploitation of their findings.…”

Section: Introductionmentioning

confidence: 99%

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Determining Clinically-Viable Biomarkers for Ischaemic Stroke Through a Mechanistic and Machine Learning Approach

Benemerito

Narata²,

Narracott

et al. 2022

Ann Biomed Eng

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Assessment of distal cerebral perfusion after ischaemic stroke is currently only possible through expensive and time-consuming imaging procedures which require the injection of a contrast medium. Alternative approaches that could indicate earlier the impact of blood flow occlusion on distal cerebral perfusion are currently lacking. The aim of this study was to identify novel biomarkers suitable for clinical implementation using less invasive diagnostic techniques such as Transcranial Doppler (TCD). We used 1D modelling to simulate pre- and post-stroke velocity and flow wave propagation in a typical arterial network, and Sobol’s sensitivity analysis, supported by the use of Gaussian process emulators, to identify biomarkers linked to cerebral perfusion. We showed that values of pulsatility index of the right anterior cerebral artery > 1.6 are associated with poor perfusion and may require immediate intervention. Three additional biomarkers with similar behaviour, all related to pulsatility indices, were identified. These results suggest that flow pulsatility measured at specific locations could be used to effectively estimate distal cerebral perfusion rates, and ultimately improve clinical diagnosis and management of ischaemic stroke.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Determining Clinically-Viable Biomarkers for Ischaemic Stroke Through a Mechanistic and Machine Learning Approach

Benemerito

Narata²,

Narracott

et al. 2022

Ann Biomed Eng

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“…collateralisation [2, 56], which lowers the chance of infarct formation. Future development should aim to incorporate such effects into computational models as proposed in [59, 60]. Without such details, simulations cover solely the ‘right tail’ of the infarct volume distribution representing the largest infarcts.…”

Section: Discussionmentioning

confidence: 99%

MRI-based computational model generation for cerebral perfusion simulations in health and ischaemic stroke

Jozsa

Petr

Barkhof

et al. 2022

Preprint

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Cerebral perfusion models were found to be promising research tools to predict the impact of acute ischaemic stroke and related treatments on cerebral blood flow (CBF) linked to patients' functional outcome. To provide insights relevant to clinical trials, perfusion simulations need to become suitable for group-level investigations, but computational studies to date have been limited to a few patient-specific cases. This study set out to overcome issues related to automated parameter inference, that restrict the sample size of perfusion simulations, by integrating neuroimaging data. Seventy-five brain models were generated using measurements from a cohort of 75 healthy elderly individuals to model resting state CBF distributions. Computational perfusion model geometries were adjusted using healthy reference subjects' T1-weighted MRI. Haemodynamic model parameters were determined from CBF measurements corresponding to arterial spin labelling perfusion MRI. Thereafter, perfusion simulations were conducted for 150 acute ischaemic stroke cases by simulating an occlusion and cessation of blood flow in the left and right middle cerebral arteries. The anatomical (geometrical) fitness of the brain models was evaluated by comparing the simulated grey and white matter (GM and WM) volumes to measurements in healthy reference subjects. Statistically significant, strong positive correlations were found in both cases (GM: Pearson's r 0.74, P-value< 0.001; WM: Pearson's r 0.84, P-value< 0.001). Haemodynamic parameter tuning was verified by comparing total volumetric blood flow rate to the brain in reference subjects and simulations resulting in Pearson's r 0.89, and P-value< 0.001. In acute ischaemic stroke cases, the simulated infarct volume using a perfusion-based proxy was 197±25 ml. Computational results showed excellent agreement with anatomical and haemodynamic literature data corresponding to T1-weighted, T2-weighted, and phase-contrast MRI measurements both in healthy scenarios and in acute ischaemic stroke cases. Simulation results represented solely worst-case stroke scenarios with large infarcts because compensatory mechanisms, e.g. collaterals, were neglected. The established computational brain model generation framework provides a foundation for population-level cerebral perfusion simulations and for in silico clinical stroke trials which could assist in medical device and drug development.

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Parameter quantification for oxygen transport in the human brain

Bing,

Józsa,

Payne

2024

Computer Methods and Programs in Biomedicine

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Modelling the leptomeningeal collateral circulation during acute ischaemic stroke

Cited by 14 publications

References 45 publications

Determining Clinically-Viable Biomarkers for Ischaemic Stroke Through a Mechanistic and Machine Learning Approach

Determining Clinically-Viable Biomarkers for Ischaemic Stroke Through a Mechanistic and Machine Learning Approach

MRI-based computational model generation for cerebral perfusion simulations in health and ischaemic stroke

Parameter quantification for oxygen transport in the human brain

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