Proper Orthogonal Decomposition Analysis of the Flow Downstream of a Dysfunctional Bileaflet Mechanical Aortic Valve

Darwish, Ahmed; Labbio, Giuseppe Di; Saleh, Wael; Kadem, Lyes

doi:10.1007/s13239-021-00519-w

Cited by 10 publications

(2 citation statements)

References 60 publications

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“…TAWSS and OSI were adequately reconstructed using only 4 and 7 POD modes in each case, capturing 96.6% and 98.7% of the normalized energy on average. Previous studies have also observed the need for higher-order modes to reconstruct OSI compared with TAWSS [ 40 ], indicating that oscillatory shear distribution is more greatly affected by lower-energy, higher-frequency modes than TAWSS. The greater differences in higher modes in the

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case indeed correspond to greater changes in oscillatory shear than flow rate-matched IVPs.…”

Section: Discussionmentioning

confidence: 99%

Aneurysmal growth in type-B aortic dissection: assessing the impact of patient-specific inlet conditions on key haemodynamic indices

Stokes,

Ahmed,

Lind

et al. 2023

J. R. Soc. Interface.

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Type-B aortic dissection is a cardiovascular disease in which a tear develops in the intimal layer of the descending aorta, allowing pressurized blood to delaminate the layers of the vessel wall. In medically managed patients, long-term aneurysmal dilatation of the false lumen (FL) is considered virtually inevitable and is associated with poorer disease outcomes. While the pathophysiological mechanisms driving FL dilatation are not yet understood, haemodynamic factors are believed to play a key role. Computational fluid dynamics (CFD) and 4D-flow MRI (4DMR) analyses have revealed correlations between flow helicity, oscillatory wall shear stress and aneurysmal dilatation of the FL. In this study, we compare CFD simulations using a patient-specific, three-dimensional, three-component inlet velocity profile (4D IVP) extracted from 4DMR data against simulations with flow rate-matched uniform and axial velocity profiles that remain widely used in the absence of 4DMR. We also evaluate the influence of measurement errors in 4DMR data by scaling the 4D IVP to the degree of imaging error detected in prior studies. We observe that oscillatory shear and helicity are highly sensitive to inlet velocity distribution and flow volume throughout the FL and conclude that the choice of IVP may greatly affect the future clinical value of simulations.

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25

case indeed correspond to greater changes in oscillatory shear than flow rate-matched IVPs.…”

Section: Discussionmentioning

confidence: 99%

Aneurysmal growth in type-B aortic dissection: assessing the impact of patient-specific inlet conditions on key haemodynamic indices

Stokes,

Ahmed,

Lind

et al. 2023

J. R. Soc. Interface.

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show abstract

“…Modal decomposition techniques have been widely used to study aerospace and industrial flow problems (Taira et al, 2017). Recent studies have applied these techniques to derive ROMs of intraventricular flow in mice (Groun et al, 2022), analyze how dysfunctional heart valves alter hemodynamics in vitro (Darwish et al, 2021;Di Labbio and Kadem), or model the flow in aneurysms (Norouzi et al, 2021;Yu and Durgesh, 2022). Flow classifiers based on ROMs have been proposed to identify distinct regimes in complex fluid problems like stratified turbulence (Ohh and Spedding, 2022).…”

Section: Discussionmentioning

confidence: 99%

Deriving Explainable Metrics of Left Ventricular Flow by Reduced-Order Modeling and Classification

Borja,

Martinez-Legazpi,

Nguyen

et al. 2023

Preprint

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BackgroundExtracting explainable flow metrics is a bottleneck to the clinical translation of advanced cardiac flow imaging modalities. We hypothesized that reduced-order models (ROMs) of intraventricular flow are a suitable strategy for deriving simple and interpretable clinical metrics suitable for further assessments. Combined with machine learning (ML) flow-based ROMs could provide new insight to help diagnose and risk-stratify patients.MethodsWe analyzed 2D color-Doppler echocardiograms of 81 non-ischemic dilated cardiomyopathy (DCM) patients, 51 hypertrophic cardiomyopathy (HCM) patients, and 77 normal volunteers (Control). We applied proper orthogonal decomposition (POD) to build patient-specific and cohort-specific ROMs of LV flow. Each ROM aggregates a low number of components representing a spatially dependent velocity map modulated along the cardiac cycle by a time-dependent coefficient. We tested three classifiers using deliberately simple ML analyses of these ROMs with varying supervision levels. In supervised models, hyperparameter gridsearch was used to derive the ROMs that maximize classification power. The classifiers were blinded to LV chamber geometry and function. We ran vector flow mapping on the color-Doppler sequences to help visualize flow patterns and interpret the ML results.ResultsPOD-based ROMs stably represented each cohort through 10-fold cross-validation. The principal POD mode captured >80% of the flow kinetic energy (KE) in all cohorts and represented the LV filling/emptying jets. Mode 2 represented the diastolic vortex and its KE contribution ranged from <1% (HCM) to 13% (DCM). Semi-unsupervised classification using patient-specific ROMs revealed that the KE ratio of these two principal modes, the vortex-to-jet (V2J) energy ratio, is a simple, interpretable metric that discriminates DCM, HCM, and Control patients. Receiver operating characteristic curves using V2J as classifier had areas under the curve of 0.81, 0.91, and 0.95 for distinguishing HCM vs. Control, DCM vs. Control, and DCM vs. HCM, respectively.ConclusionsModal decomposition of cardiac flow can be used to create ROMs of normal and pathological flow patterns, uncovering simple interpretable flow metrics with power to discriminate disease states, and particularly suitable for further processing using ML.

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Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond

et al. 2022

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Proper Orthogonal Decomposition Analysis of the Flow Downstream of a Dysfunctional Bileaflet Mechanical Aortic Valve

Cited by 10 publications

References 60 publications

Aneurysmal growth in type-B aortic dissection: assessing the impact of patient-specific inlet conditions on key haemodynamic indices

Aneurysmal growth in type-B aortic dissection: assessing the impact of patient-specific inlet conditions on key haemodynamic indices

Deriving Explainable Metrics of Left Ventricular Flow by Reduced-Order Modeling and Classification

Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond

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