Vector flow mapping: A review from theory to practice

Avesani, Martina; Degrelle, Bastien; Salvo, Giovanni Di; Thambo, Jean-Benoît; Iriart, Xavier

doi:10.1111/echo.15154

Cited by 15 publications

(16 citation statements)

References 62 publications

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“…While the impacts of LV flow patterns and pathlines in cardiac mechanical energy balance have been a subject of debate (Elbaz et al, 2017; Watanabe et al, 2008), there is consensus of their potential as imaging biomarkers of cardiac health. This potential has stimulated significant developments in flow imaging, such as 4D flow cardiac magnetic resonance (Dyverfeldt et al, 2015), or echocardiographic vector flow mapping (VFM) (Avesani et al, 2021; Garcia et al, 2010) and blood speckle tracking (Daae et al, 2021). However, the significant intra- and inter-patient variability of LV flow dynamics (Bermejo et al, 2014; Sundin et al, 2020) make it difficult to define the normal pattern by narrowly constrained numerical metrics, or quantifying how these metrics are altered in different cardiomyopathies.…”

Section: Discussionmentioning

confidence: 99%

“…To enhance the interpretability of the reduced-order models and associated classifiers, we derived time-resolved 2D blood velocity fields from color-Doppler data inside the LV using vector flow mapping (VFM), an algorithm thoroughly described in previous studies (Avesani et al, 2021; Garcia et al, 2010; Hvid et al, 2023; Minami et al, 2021). The VFM algorithm is fed by a color-Doppler acquisition and integrates the continuity equation imposing no-penetration boundary conditions at the LV endocardium segmented from the 2D cine-loops.…”

Section: Methodsmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

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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“…For patients with suspected stable angina but no known coronary artery disease, simultaneous application of exercise stress echocardiography and carotid ultrasound will provide more reliable disease prognosis value 7 . Vector blood flow mapping (VFM) combines color Doppler and speckle tracking imaging to display the local blood flow velocity vector in the cardiac cavity and can quantify the energy loss and myocardial mechanics, thus achieving a noninvasive assessment of intracardiac blood flow 8,9 . Myocardial ischemia caused by coronary artery stenosis is statistically shown to generate abnormal myocardial energy metabolism, affect myocardial contraction and relaxation performance, reduce the coordination of the overall motion of the ventricular wall and increase cardiac afterload, consequently resulting in irreversible changes in the intracardiac blood flow field and hemodynamics.…”

Section: Introductionmentioning

confidence: 99%

“…7 Vector blood flow mapping (VFM) combines color Doppler and speckle tracking imaging to display the local blood flow velocity vector in the cardiac cavity and can quantify the energy loss and myocardial mechanics, thus achieving a noninvasive assessment of intracardiac blood flow. 8,9 Myocardial ischemia caused by coronary artery stenosis is statistically shown to generate abnormal myocardial energy metabolism, affect myocardial contraction and relaxation performance, reduce the coordination of the overall motion of the ventricular wall and increase cardiac afterload, con-sequently resulting in irreversible changes in the intracardiac blood flow field and hemodynamics. There is still no current research on how mild coronary artery stenosis in patients changes the left ventricular flow field after exercise stress.…”

Section: Introductionmentioning

confidence: 99%

Assessment of left ventricular energy loss in patients with mild coronary artery stenosis by using vector flow mapping combined with exercise stress echocardiography

et al. 2023

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Objectives To evaluate the left ventricular energy loss (EL), energy loss reserve (EL‐r), and energy loss reserve rate in patients with mild coronary artery stenosis by using vector flow mapping (VFM) combined with exercise stress echocardiography. Methods A total of 34 patients (case group) with mild coronary artery stenosis and 36 sex and age matched patients (control group) without coronary artery stenosis according to coronary angiogram were prospectively enrolled. The total energy loss (ELt), basal segment energy loss (ELb), middle segment energy loss (ELm), apical segment energy loss (ELa), energy loss reserve (EL‐r), and energy loss reserve rate were recorded in the isovolumic systolic period (S1), rapid ejection period (S2), slow ejection period (S3), isovolumic diastolic period (D1), rapid filling period (D2), slow filling period (D3), and atrial contraction period (D4). Results Compared with the control group, some of the EL in the resting case group were higher; some of the EL in the case group were lower after exercise, and those during D1 ELb and D3 ELb were higher. Compared with the resting state, the total EL and the EL within the time segment in the control group were higher after exercise, except during D2 ELb. In the case group, except for during D1 ELt, ELb and D2 ELb, the total and segmental EL of each phase was mostly higher after exercise (p < .05). Compared with the control group, most of the EL‐r and EL reserve rates in the case group were lower (p < .05). Conclusion The EL, EL‐r, and energy loss reserve rate have a certain value in the evaluation of cardiac function in patients with mild coronary artery stenosis.

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“…4 Another echocardiographic method, which use STE, is vector flow mapping (VFM). Vector flow mapping combine Color-Doppler and STE data for quantifying energy loss, 6 which indirectly reflects cardiac effort. Clinical applications of VFM are impaired diastolic and systolic function, cardiomyopathies, valvular diseases (aortic and mitral valve), rhythm disorders, congenital heart diseases.…”

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confidence: 99%

Subclinical systolic dysfunction and myocardial deformation: From GLS to VVI

Floria,

Tanase

2023

J of Clinical Ultrasound

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Left ventricular (LV) systolic function can be evaluated using echocardiography by ejection fraction (by LV volume in 2D and 3D methods), Tissue Doppler Imaging or myocardial strain by 2D or 3D speckle tracking echocardiography-STE, global longitudinal strain-GLS, radial strain-GRS, and circumferential strain-GCS, and associated peak strain rates (GLSr, GRSr, GCSr). Besides longitudinal myocardial deformation, radial, and circumferential myocardial deformation is important in understanding myocardial motion in the short-axis direction. In addition, there are some new derived STE methods, which are not part of the daily clinical practice. Novel techniques derived from STE have gained advantages in overcoming the disadvantages of classical deformation methods. However, Tissue Doppler Imaging was the first method used for directly measuring myocardial deformation by echocardiography. Due to the angle dependency, it is now less used, being replaced by GLS and vector velocity imaging (VVI). There is no echocardiographic technique in 2D or 3D, which is perfect, each of them having advantages and disadvantages (Figure 1). Jianwei Shi et al1 assessed the LV systolic function in patients with Parkinson's disease by LV ejection fraction, GLS, GRS, and GCS, and their associated peak strain rates. They analyzed the correlation between LV dysfunction and the severity of Parkinson's disease symptoms using the modified Hoehn-Yahr scale. This score and the Parkinson's disease duration were correlated with reduced LV systolic function. The severity of LV impairment was influenced by blood pressure, heart rate variability, and blood glucose. The authors considered that it is feasible to assess subclinical LV global systolic dysfunction in patients with Parkinson's disease by VVI. 1 This study is very important for clinical practice because monitoring the cardiac function in patients with Parkinson disease, the second most prevalent neurodegenerative disease, will allow the detection of the progression of this disease.

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Vector flow mapping: A review from theory to practice

Cited by 15 publications

References 62 publications

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

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

Assessment of left ventricular energy loss in patients with mild coronary artery stenosis by using vector flow mapping combined with exercise stress echocardiography

Subclinical systolic dysfunction and myocardial deformation: From GLS to VVI

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