Prediction of cerebral perfusion pressure during carotid surgery – A computational fluid dynamics approach

Holmgren, Madelene; Holmlund, Petter; Støverud, Karen-Helene; Zarrinkoob, Laleh; Wåhlin, Anders; Malm, Jan; Eklund, Anders

doi:10.1016/j.clinbiomech.2022.105827

Cited by 4 publications

(8 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different simulation models for cerebral hemodynamics have been proposed in the literature, which can be categorised based on the dimensionality of the modelling approach. 3D models, also referred to as computational fluid dynamics (CFD) models, were shown to be a powerful tool for numerical representation of complex flow patterns in single cerebral arteries or lesions in ICAD (20,(46)(47)(48), intracranial aneurysms (49), or moyamoya disease (50). These studies identified flow abnormalities such as increased wall shear stress, elevated plaque steepness or low fractional flow as indicators of hemodynamic impairment.…”

Section: Discussionmentioning

confidence: 99%

“…In ICAD, promising image-based methods for hemodynamic assessment are personalised simulations of cerebral blood flow. Using these methods, hemodynamic patterns have emerged that could prove valuable as individual biomarkers of disease progression in ICAD (19)(20)(21)(22). These tools utilise structural brain imaging data for mechanistic simulations and allow for an individual assessment of hemodynamic impairment, collateral status, stroke vulnerability and treatment response.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Personalised simulation of hemodynamics in cerebrovascular disease: lessons learned from a study of diagnostic accuracy

Behland,

Madai,

Aydin

et al. 2023

Front. Neurol.

View full text Add to dashboard Cite

Intracranial atherosclerotic disease (ICAD) poses a significant risk of subsequent stroke but current prevention strategies are limited. Mechanistic simulations of brain hemodynamics offer an alternative precision medicine approach by utilising individual patient characteristics. For clinical use, however, current simulation frameworks have insufficient validation. In this study, we performed the first quantitative validation of a simulation-based precision medicine framework to assess cerebral hemodynamics in patients with ICAD against clinical standard perfusion imaging. In a retrospective analysis, we used a 0-dimensional simulation model to detect brain areas that are hemodynamically vulnerable to subsequent stroke. The main outcome measures were sensitivity, specificity, and area under the receiver operating characteristics curve (ROC AUC) of the simulation to identify brain areas vulnerable to subsequent stroke as defined by quantitative measurements of relative mean transit time (relMTT) from dynamic susceptibility contrast MRI (DSC-MRI). In 68 subjects with unilateral stenosis >70% of the internal carotid artery (ICA) or middle cerebral artery (MCA), the sensitivity and specificity of the simulation were 0.65 and 0.67, respectively. The ROC AUC was 0.68. The low-to-moderate accuracy of the simulation may be attributed to assumptions of Newtonian blood flow, rigid vessel walls, and the use of time-of-flight MRI for geometric representation of subject vasculature. Future simulation approaches should focus on integrating additional patient data, increasing accessibility of precision medicine tools to clinicians, addressing disease burden disparities amongst different populations, and quantifying patient benefit. Our results underscore the need for further improvement of mechanistic simulations of brain hemodynamics to foster the translation of the technology to clinical practice.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Personalised simulation of hemodynamics in cerebrovascular disease: lessons learned from a study of diagnostic accuracy

Behland,

Madai,

Aydin

et al. 2023

Front. Neurol.

View full text Add to dashboard Cite

show abstract

“…Another, clinically relevant, modelling application are predictions of cerebral blood ow during surgeries where cerebral blood ow may be compromised. By estimating a patient's hemodynamical properties prior to surgery, the risk of hypoperfusion can be assessed which could help guide intraoperative decision-making [14]. As blood ow is what is of interest for such predictions it is suitable to make assumptions on the surgical situation based on resistance.…”

Section: Value Of Territorial Resistances For Modelling and Predictionsmentioning

confidence: 99%

“…In the non-stenotic group, lacking signi cant stenoses, MAP was assumed in the ICA with the largest ow rate. For subjects in the stenotic group with a contralateral stenosis ≥ 50%, the pressure at the contralateral ICA was found by subtracting the pressure drop across the stenosis from the MAP, where the pressure drop was computed in a separate CFD simulation of the stenosis [14].…”

Section: Cfd Simulationsmentioning

confidence: 99%

“…CVR is mostly estimated globally [12,13], usually due to the simplicity of measuring total cerebral in ow and mean arterial pressure, as opposed to the local ow rates and perfusion pressures needed to determine CVR of each vascular territory. Knowing these territorial resistances would not only improve our understanding of physiology and pathophysiology, but could also be used for predictions of blood ow and perfusion pressure during planned interventions such as medical or surgical treatments [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Establishing the distribution of cerebrovascular resistance using computational fluid dynamics and 4D flow MRI

Vikström,

Holmlund,

Holmgren

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Cerebrovascular resistance (CVR) regulates blood flow in the brain, but little is known about the vascular resistances of the individual cerebral territories. We present a method to calculate these resistances and investigate how CVR varies in the hemodynamically disturbed brain. We included 48 patients with stroke/TIA (29 with symptomatic carotid stenosis). By combining flow rate (4D flow MRI) and structural computed tomography angiography (CTA) data, and using computational fluid dynamics (CFD) we computed the perfusion pressures out from the circle of Willis, with which CVR of the MCA, ACA, and PCA territories was estimated. 56 controls were included for comparison of total CVR. CVR were 33.8 ± 10.5, 59.0 ± 30.6, and 77.8 ± 21.3 mmHg·s/ml for the MCA, ACA, and PCA territories. We found no differences in total CVR between patients, 9.3 ± 1.9 mmHg·s/ml, and controls, 9.3 ± 2.0 mmHg·s/ml (p = 0.88), nor in territorial CVR in the carotid stenosis patients between ipsilateral and contralateral hemispheres. Territorial resistance associated inversely to territorial brain volume (p < 0.001). These resistances may work as reference values when modelling blood flow in the circle of Willis, and the method can be used when there is need for subject-specific analysis.

show abstract

Numerical Modeling of Flow in the Cerebral Vasculature: Understanding Changes in Collateral Flow Directions in the Circle of Willis for a Cohort of Vasospasm Patients Through Image-Based Computational Fluid Dynamics

Straccia,

Barbour,

Chassagne

et al. 2024

Ann Biomed Eng

View full text Add to dashboard Cite

Prediction of cerebral perfusion pressure during carotid surgery – A computational fluid dynamics approach

Cited by 4 publications

References 35 publications

Personalised simulation of hemodynamics in cerebrovascular disease: lessons learned from a study of diagnostic accuracy

Personalised simulation of hemodynamics in cerebrovascular disease: lessons learned from a study of diagnostic accuracy

Establishing the distribution of cerebrovascular resistance using computational fluid dynamics and 4D flow MRI

Numerical Modeling of Flow in the Cerebral Vasculature: Understanding Changes in Collateral Flow Directions in the Circle of Willis for a Cohort of Vasospasm Patients Through Image-Based Computational Fluid Dynamics

Contact Info

Product

Resources

About