The value of non-invasive vascular elastography (NIVE) in detecting early vascular changes in overweight and obese children

Jalbout, Ramy El; Cloutier, Guy; Cardinal, Marie-Hélène Roy; Henderson, Mélanie; Lévy, Émile; Lapierre, Chantale; Soulez, Gilles; Dubois, Josée

doi:10.1007/s00330-019-06051-9

Cited by 15 publications

(14 citation statements)

References 31 publications

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“…To monitor these changes, ultrasound imaging, a low cost, noninvasive and easily accessible modality has been the platform on which a multitude of local arterial elasticity imaging techniques have been developed [9]. Vascular Elastography [10][11][12][13][14][15][16][17][18][19] Acoustic Radiation Force Imaging (ARFI) [20] and Shear Wave Elastography (SWE) [21][22][23][24] are methods that have shown great promise in vascular disease diagnosis by depicting the wall's mechanical properties.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Pulse Wave Imaging: Automated Spatial Vessel Wall Inhomogeneity Detection in Phantoms and in-Vivo

Apostolakis

Karageorgos

Nauleau

et al. 2020

IEEE Trans. Med. Imaging

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Imaging arterial mechanical properties may improve vascular disease diagnosis. Pulse wave velocity (PWV) is a marker of arterial stiffness linked to cardiovascular mortality. Pulse wave imaging (PWI) is a technique for imaging the pulse wave propagation at high spatial and temporal resolution. In this study, we introduce adaptive PWI, a technique for the automated partition of heterogeneous arteries into individual segments characterized by most homogeneous pulse wave propagation, allowing for more robust PWV estimation. This technique was validated in a silicone phantom with a soft-stiff interface. The mean detection error of the interface was 4.67±0.73 mm and 3.64±0.14 mm in the stiff-to-soft and soft-to-stiff pulse wave transmission direction, respectively. This technique was tested in monitoring the progression of atherosclerosis in mouse aortas in vivo (n = 11). PWV was found to already increase at the early stage of 10 weeks of highfat diet (3.17±0.67m/sec compared to baseline 2.55±0.47m/sec, p < 0.05) and further increase after 20 weeks of high-fat diet (3.76±1.20m/sec). The number of detected segments of the imaged aortas monotonically increased with the duration of high-fat diet indicating an increase in arterial wall property inhomogeneity. The performance of adaptive PWI was also tested in aneurysmal mouse aortas in vivo. Aneurysmal boundaries were detected with a mean error of 0.68±0.44mm. Finally, initial feasibility was shown in the carotid arteries of healthy and atherosclerotic human subjects in vivo (n = 3 each). Consequently, adaptive PWI was successful in detecting stiffness inhomogeneity at its early onset and monitoring atherosclerosis progression in vivo.

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Section: Introductionmentioning

confidence: 99%

Adaptive Pulse Wave Imaging: Automated Spatial Vessel Wall Inhomogeneity Detection in Phantoms and in-Vivo

Apostolakis

Karageorgos

Nauleau

et al. 2020

IEEE Trans. Med. Imaging

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“…Another limitation, related to the use of machine learning, is the lack of mechanistic interpretation to explain the association between the presence of plaque and selected features because linear relations between the model output and inputs are not measured with RF. Nonetheless, carotid artery translation and strain parameters used here were shown to be of value to assess premature atherosclerosis in other studies 16,17,50 and advanced plaques of symptomatic and asymptomatic patients with carotid stenoses. [51][52][53] The new US imaging arsenal proposed here for assessing artery wall biomechanics may be of value to study noninvasively plaque progression and stroke risk in PLWH (and in the general population).…”

Section: Study Limitationsmentioning

confidence: 72%

“…16 Other US-based approaches for indirectly assessing vascular wall elasticity have also found increased arterial rigidity in PLWH. [24][25][26] Because US strain imaging could detect premature atherosclerosis better than cIMT in PLWH and other populations, 16,17 we investigated if it could be associated with plaque presence in PLWH having low-to-moderate cardiovascular risk. For this purpose, a machine learning strategy was used because it has been shown to significantly improve the accuracy of cardiovascular risk prediction compared with traditional statistical models.…”

Section: Introductionmentioning

confidence: 99%

Associative Prediction of Carotid Artery Plaques Based on Ultrasound Strain Imaging and Cardiovascular Risk Factors in People Living With HIV and Age-Matched Control Subjects of the CHACS Cohort

Cardinal

Durand

Chartrand-Lefèbvre

et al. 2022

JAIDS Journal of Acquired Immune Deficiency Syndromes

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for the Canadian HIV and Aging Cohort StudyBackground: There is a need for a specific atherosclerotic risk assessment for people living with HIV (PLWH).Setting: A machine learning classification model was applied to PLWH and control subjects with low-to-intermediate cardiovascular risks to identify associative predictors of diagnosed carotid artery plaques. Associations with plaques were made using strain elastography in normal sections of the common carotid artery and traditional cardiovascular risk factors.Methods: One hundred two PLWH and 84 control subjects were recruited from the prospective Canadian HIV and Aging Cohort Study (57 6 8 years; 159 men). Plaque presence was based on clinical ultrasound scans of left and right common carotid arteries and internal carotid arteries. A classification task for identifying subjects with plaque was defined using random forest (RF) and logistic regression models. Areas under the receiver operating characteristic curves (AUC-ROCs) were applied to select 5 among 50 combinations of 4 or less features yielding the highest AUC-ROCs. Results:To retrospectively classify individuals with and without plaques, the 5 most discriminant combinations of features had AUC-ROCs between 0.76 and 0.79. AUC-ROCs from RF were statistically significantly higher than those obtained with logistic regressions (P = 0.0001). The most discriminant features of RF classifications in PLWH were age, smoking status, maximum axial strain and pulse pressure (equal weights), and sex and antiretroviral therapy exposure (equal weights). When considering the whole population, the HIV status was identified as a cofactor associated with carotid artery plaques.Conclusions: Strain elastography adds to traditional cardiovascular risk factors for identifying individuals with carotid artery plaques.

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“…Six strain, shear strain, and translation features are reported 17–19 . They quantify different components of the vessel wall motion.…”

Section: Methodsmentioning

confidence: 99%

“…Six strain, shear strain, and translation features are reported. [17][18][19] They quantify different components of the vessel wall motion. The first two features are the maximum axial strain (MaxAS) and cumulated axial strain (CAS); they correspond respectively to the peak and global compression or dilatation of the vessel wall during a cardiac cycle.…”

Section: Study Protocolmentioning

confidence: 99%

Impact of Applying a Skin Compression With the Ultrasound Probe on Carotid Artery Strain Elastography

Chayer

Cardinal

Biron

et al. 2021

J of Ultrasound Medicine

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Objective To study the impact of varying the external compression exerted by the ultrasound probe when performing a carotid strain elastography exam. Methods Nine healthy volunteers (mean age 43 years ±13 years; 6 men) underwent a vascular ultrasound elastography exam using a custom made sound feedback handle embedding the probe, and allowing the sonographer to adjust the applied compression. A clinical standard practice (SP) force was first recorded, and then predetermined compression (PDC) forces were applied, ranging from 0 to 5 N for the left common carotid artery (CCA) or 2–12 N for the left internal carotid artery (ICA). Six carotid elastography features, namely maximum and cumulated axial strains, maximum and cumulated shear strains, cumulated axial translation, and cumulated lateral translation were assessed with noninvasive vascular elastography (NIVE) on near and far walls of carotids. The carotid intima media thickness (IMT) and diameter were also measured. Results All elastography features on the near wall of both CCA and ICA decreased statistically significantly as the PDC force increased; this association was also observed for half of the features on the far wall. Three NIVE features at the lowest PDC force (out of 72 that were tested) were statistically significantly different than values at the SP force. Overall, NIVE showed some variance to probe compression with linear regression slopes revealing changes of 10.1%–45.6% in magnitude over the whole compression range on both walls. The maximum IMT for the ICA near wall, and carotid lumen diameters of both CCA and ICA were statistically significantly associated with PDC forces; these features underwent a decrease of 10.2%, 36.2%, and 17.6%, respectively, over the whole range of PDC force increase. Other IMT measurements were not statistically significantly associated with applied PDC forces. Conclusion These results suggest the need of technical guidelines for carotid strain elastography. Using the lowest probe compression while allowing a good B‐mode image quality is recommended to improve the robustness of NIVE measurements.

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The value of non-invasive vascular elastography (NIVE) in detecting early vascular changes in overweight and obese children

Cited by 15 publications

References 31 publications

Adaptive Pulse Wave Imaging: Automated Spatial Vessel Wall Inhomogeneity Detection in Phantoms and in-Vivo

Adaptive Pulse Wave Imaging: Automated Spatial Vessel Wall Inhomogeneity Detection in Phantoms and in-Vivo

Associative Prediction of Carotid Artery Plaques Based on Ultrasound Strain Imaging and Cardiovascular Risk Factors in People Living With HIV and Age-Matched Control Subjects of the CHACS Cohort

Impact of Applying a Skin Compression With the Ultrasound Probe on Carotid Artery Strain Elastography

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