A data-driven approach for addressing the lack of flow waveform data in studies of cerebral arterial flow in older adults

Durka, Michael J.; Wong, Isaac H; Kallmes, David F.; Pasalic, Dario; Mut, Fernando; Blanco, Pablo; Cebral, Juan R.; Robertson, Anne M.

doi:10.1088/1361-6579/aa9f46

Cited by 19 publications

(14 citation statements)

References 34 publications

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“…To define a range of inflow boundary conditions, we used data from flow measurements in patients and healthy volunteers. For the internal carotid artery (ICA), we used Doppler ultrasound data of measurements in 136 older patients (patients without cerebral aneurysms) from Durka et al [10] and 2D cine phase-contrast (PC) magnet resonance imaging (MRI) data of 17 healthy volunteers from Ford et al [11] (see Fig. 1, left).…”

Section: Flow Datamentioning

confidence: 99%

“…The aim of this study was therefore an assessment of the influence of altered inflow conditions of the CFD simulations on the computed hemodynamics and the development of a strategy for incorporating these flow-dependencies into a multivariate statistical model for rupture prediction that is robust with respect to inflow variabilities. For this purpose, flow variations were divided into inter-and intra-patient variations, of which the range was defined based on flow measurements in patients and healthy subjects [10,11,21].…”

Section: Introductionmentioning

confidence: 99%

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Incorporating variability of patient inflow conditions into statistical models for aneurysm rupture assessment

et al. 2020

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Background: Hemodynamic patterns have been associated with cerebral aneurysm instability. For patient-specific computational fluid dynamics (CFD) simulations, the inflow rates of a patient are typically not known. The aim of this study was to analyze the influence of inter-and intrapatient variations of cerebral blood flow on the computed hemodynamics through CFD simulations and to incorporate these variations into statistical models for aneurysm rupture prediction. Methods: Image data of 1,820 aneurysms were used for patient-specific steady CFD simulations with nine different inflow rates per case, capturing inter-and intra-patient flow variations. Based on the computed flow fields, 17 hemodynamic parameters were calculated and compared for the different flow conditions. Next, statistical models for aneurysm rupture were trained in 1,571 of the aneurysms including hemodynamic parameters capturing the flow-variations either by defining hemodynamic "response variables" (model A) or repeatedly randomly selecting flow conditions by patients (model B) as well as morphological and patient-specific variables. Both models were evaluated in the remaining 249 cases. Results: All hemodynamic parameters were significantly different for the varying flow conditions (p<0.001). Both the flow-independent "response" model A and the flow-dependent model B performed well with areas under the receiver operating characteristic curve of 0.8182 and 0.8174±0.0045, respectively. Conclusions: The influence of inter-and intra-patient flow variations on computed hemodynamics can be taken into account in multivariate aneurysm rupture prediction models

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Section: Flow Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Incorporating variability of patient inflow conditions into statistical models for aneurysm rupture assessment

et al. 2020

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“…In such cases, using normative flow values may be a necessity, but the uncertainty created by missing patient‐specific measurements should be properly quantified. Recent studies have looked at between‐subjects cerebral flow variability by combining data and mathematical modelling to quantify the CBF uncertainty. We developed a data‐driven between‐subjects flow variability model for ICA flow in elderly dementia patients and age‐matched controls.…”

Section: Discussionmentioning

confidence: 99%

“…This, in turn, will lead to overconfidence in vascular CFD uncertainty quantification. More recently, statistical models of carotid flow variability have been proposed in the literature and used to perform uncertainty quantification of vascular CFD. While between‐subjects flow variability is relatively straightforward to quantify by repeated measurements in a suitably selected cohort, a more informative measure would be within‐subject flow variability, ie, multiple flow measurements in the same subject but over a series of physiological states (eg, rest vs exercise).…”

Section: Introductionmentioning

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

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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

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“…All of these processes are currently the subject of multiscale modeling at varying degrees of sophistication (Anderson and Vadigepalli, 2016 ; Linninger et al, 2016 ; Calvetti et al, 2018 ; Durka et al, 2018 ; Zhao et al, 2018 ). Although it would not be practical to incorporate these many types of simulation within NEURON, there will be possibilities for cross-simulator communication providing complex multiphysics simulations in the future (Djurfeldt et al, 2010 ).…”

Section: Discussionmentioning

confidence: 99%

Using NEURON for Reaction-Diffusion Modeling of Extracellular Dynamics

Newton

McDougal

Hines

et al. 2018

Front. Neuroinform.

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Development of credible clinically-relevant brain simulations has been slowed due to a focus on electrophysiology in computational neuroscience, neglecting the multiscale whole-tissue modeling approach used for simulation in most other organ systems. We have now begun to extend the NEURON simulation platform in this direction by adding extracellular modeling. The extracellular medium of neural tissue is an active medium of neuromodulators, ions, inflammatory cells, oxygen, NO and other gases, with additional physiological, pharmacological and pathological agents. These extracellular agents influence, and are influenced by, cellular electrophysiology, and cellular chemophysiology—the complex internal cellular milieu of second-messenger signaling and cascades. NEURON's extracellular reaction-diffusion is supported by an intuitive Python-based where/who/what command sequence, derived from that used for intracellular reaction diffusion, to support coarse-grained macroscopic extracellular models. This simulation specification separates the expression of the conceptual model and parameters from the underlying numerical methods. In the volume-averaging approach used, the macroscopic model of tissue is characterized by free volume fraction—the proportion of space in which species are able to diffuse, and tortuosity—the average increase in path length due to obstacles. These tissue characteristics can be defined within particular spatial regions, enabling the modeler to account for regional differences, due either to intrinsic organization, particularly gray vs. white matter, or to pathology such as edema. We illustrate simulation development using spreading depression, a pathological phenomenon thought to play roles in migraine, epilepsy and stroke. Simulation results were verified against analytic results and against the extracellular portion of the simulation run under FiPy. The creation of this NEURON interface provides a pathway for interoperability that can be used to automatically export this class of models into complex intracellular/extracellular simulations and future cross-simulator standardization.

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A data-driven approach for addressing the lack of flow waveform data in studies of cerebral arterial flow in older adults

Cited by 19 publications

References 34 publications

Incorporating variability of patient inflow conditions into statistical models for aneurysm rupture assessment

Incorporating variability of patient inflow conditions into statistical models for aneurysm rupture assessment

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

Using NEURON for Reaction-Diffusion Modeling of Extracellular Dynamics

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