Screening for Cognitive Impairment by Model-Assisted Cerebral Blood Flow Estimation

Lassila, Toni; Marco, Luigi Yuri Di; Mitolo, Micaela; Iaia, Vincenzo; Levedianos, Giorgio; Venneri, Annalena; Frangi, Alejandro F.

doi:10.1109/tbme.2017.2759511

Cited by 13 publications

(18 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The remaining component of the subject-specific modelling pipeline is the personalized BCs for arterial blood flow. A subject-specific characterization of 24 h blood flow variability is obtained through a combination of ambulatory blood pressure measurements, clinical ultrasound flow measurements and mathematical modelling [ 38 ]. A lumped parameter circulation model (LPCM) [ 39 ] is used to simulate continuous arterial blood flow and translate spot measurements collected at 15 min intervals to continuous waveforms of arterial blood flow.…”

Section: Methodsmentioning

confidence: 99%

Subject-specific multi-poroelastic model for exploring the risk factors associated with the early stages of Alzheimer's disease

et al. 2017

Self Cite

View full text Add to dashboard Cite

There is emerging evidence suggesting that Alzheimer's disease is a vascular disorder, caused by impaired cerebral perfusion, which may be promoted by cardiovascular risk factors that are strongly influenced by lifestyle. In order to develop an understanding of the exact nature of such a hypothesis, a biomechanical understanding of the influence of lifestyle factors is pursued. An extended poroelastic model of perfused parenchymal tissue coupled with separate workflows concerning subject-specific meshes, permeability tensor maps and cerebral blood flow variability is used. The subject-specific datasets used in the modelling of this paper were collected as part of prospective data collection. Two cases were simulated involving male, non-smokers (control and mild cognitive impairment (MCI) case) during two states of activity (high and low). Results showed a marginally reduced clearance of cerebrospinal fluid (CSF)/interstitial fluid (ISF), elevated parenchymal tissue displacement and CSF/ISF accumulation and drainage in the MCI case. The peak perfusion remained at 8 mm s −1 between the two cases.

show abstract

Section: Methodsmentioning

confidence: 99%

Subject-specific multi-poroelastic model for exploring the risk factors associated with the early stages of Alzheimer's disease

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ageing and lifestyle related patient-specific boundary conditions are generated following the data collection and subject-based model parameterisation. The personalisation of the lumped parameter circulation model (LPCM) was accelerated via a surrogate model to approximate its input-output response (Lassila et al, 2018). LPCM = Lumped parameter circulation model (based on Ursino, 1998), CAM = cerebral autoregulation model (based on Mader et al, 2015).…”

Section: Three-dimensional Mpet Model For the Cerebral Environmentmentioning

confidence: 99%

“…Further details can be found in Guo et al (2018). For the Lido study cohort, Holter recordings and ultrasound flow measurements were used to generate boundary conditions of arterial blood flow using cerebral autoregulation models and lumped parameter circulation models (Guo et al, 2018;Lassila et al, 2018), T1-weighted and diffusion-weighted MR images were processed to create accurate 3D whole-brain meshes and finally permeability tensor maps of the parenchyma were extracted using the workflow described in detail in Guo et al (2018).…”

Section: Subject-specific Datasetsmentioning

confidence: 99%

“…In Fig. 4, the subject-specific modelling pipeline for acquiring personalised cerebral blood flow waveforms (Lassila et al, 2018) that are fed into the arterial compartment of the MPET model (Guo et al, 2018) is depicted. For each subject, four waveforms were calculated at every time point, which are the ICA blood to the left and right cerebrum (ICA L and ICA R ), and the vertebral artery (VA) blood to the left and right cerebellum (VA L and VA R ).…”

Section: Subject-specific Boundary Conditions and Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Fluid–structure interaction for highly complex, statistically defined, biological media: Homogenisation and a 3D multi-compartmental poroelastic model for brain biomechanics

Vardakis

Guo

Peach

et al. 2019

Journal of Fluids and Structures

Self Cite

View full text Add to dashboard Cite

“…Patient‐specific carotid flow data ( Lido cohort ) used in this study were part of an Alzheimer disease study conducted at the Istituto di Ricovero e Cura a Carattere Scientifico San Camillo, Lido di Venezia, Italy, and previously reported in Lassila et al The cohort included 103 elderly people (age 73±7 years), of whom 53 were diagnosed with mild cognitive impairment, and the rest were healthy controls. Exclusion criteria included cerebrovascular disease as main aetiology, as well as the presence of any cardiovascular disease.…”

Section: Methodsmentioning

confidence: 99%

Population‐specific modelling of between/within‐subject flow variability in the carotid arteries of the elderly

Lassila

Sarrami‐Foroushani

Hejazi

et al. 2019

Numer Methods Biomed Eng

Self Cite

View full text Add to dashboard Cite

Computational fluid dynamics models are increasingly proposed for assisting the diagnosis and management of vascular diseases. Ideally, patient-specific flow measurements are used to impose flow boundary conditions. When patient-specific flow measurements are unavailable, mean values of flow measurements across small cohorts are used as normative values. In reality, both the between-subjects and within-subject flow variabilities are large. Consequently, neither one-shot flow measurements nor mean values across a cohort are truly indicative of the flow regime in a given person. We develop models for both the between-subjects and within-subject variability of internal carotid flow. A log-linear mixed effects model is combined with a Gaussian process to model the between-subjects flow variability, while a lumped parameter model of cerebral autoregulation is used to model the within-subject flow variability in response to heart rate and blood pressure changes. The model parameters are identified from carotid ultrasound measurements in a cohort of 103 elderly volunteers. We use the models to study intracranial aneurysm flow in 54 subjects under rest and exercise and conclude that OSI, a common wall shear-stress derived quantity in vascular CFD studies, may be too sensitive to flow fluctuations to be a reliable biomarker.KEYWORDS cerebrovascular disease, computational fluid dynamics, Gaussian process models, patient-specific models, uncertainty quantification Numerous computational fluid dynamics (CFD) models of human cardiovascular and cerebrovascular physiology are published every year, but few of them make any impact in clinical practice. This inconvenient truth has been blamed on the improper use of CFD solvers, 1 insufficient validation of biomechanics models, 2 and lack of understanding of the clinical decision-making process by the biomedical engineers building the models. 3 One additional explanation is that many "patient-specific" CFD models fail to consider the physiological variability of vascular flow, confounding interpretation of model results and producing overly confident predictions of flow quantities.Patient-specific modelling of vascular flow requires an accurate description of the lumen plus the definition of boundary conditions. The latter is done by measuring patient-specific flow waveforms using either phase contrast magnetic resonance imaging (pcMRI) or ultrasound-based flow measurement techniques. When patient-specific flow measurements are not available, cohort-averaged values of flow from the literature are often used. For example, the small-scale studies 4-6 Int J Numer Meth Biomed Engng. 2020;36:e3271.wileyonlinelibrary.com/journal/cnm

show abstract

Screening for Cognitive Impairment by Model-Assisted Cerebral Blood Flow Estimation

Cited by 13 publications

References 35 publications

Subject-specific multi-poroelastic model for exploring the risk factors associated with the early stages of Alzheimer's disease

Subject-specific multi-poroelastic model for exploring the risk factors associated with the early stages of Alzheimer's disease

Fluid–structure interaction for highly complex, statistically defined, biological media: Homogenisation and a 3D multi-compartmental poroelastic model for brain biomechanics

Population‐specific modelling of between/within‐subject flow variability in the carotid arteries of the elderly

Contact Info

Product

Resources

About