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

Apostolakis, Iason; Karageorgos, Grigorios Marios; Nauleau, Pierre; Nandlall, Sacha D.; Konofagou, Elisa E.

doi:10.1109/tmi.2019.2926141

Cited by 22 publications

(17 citation statements)

References 54 publications

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“…The difference in transit time is directly proportional to velocity and by dividing the measured distance by the difference in transit time of the two waveforms, the PWV can be calculated. An alternative ultrasound-based method is pulse wave velocity imaging (PWI), which uses the detection of vessel wall motion [ 93 ]. In the clinic, however, the evaluation of the carotid-femoral PWV (cfPWV) and the brachial-ankle PWV (baPWV) are most common [ 94 ].…”

Section: Arterial Wall Stiffness In Atherosclerosismentioning

confidence: 99%

Evaluation of Plaque Characteristics and Inflammation Using Magnetic Resonance Imaging

et al. 2021

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Atherosclerosis is an inflammatory disease of large and medium-sized arteries, characterized by the growth of atherosclerotic lesions (plaques). These plaques often develop at inner curvatures of arteries, branchpoints, and bifurcations, where the endothelial wall shear stress is low and oscillatory. In conjunction with other processes such as lipid deposition, biomechanical factors lead to local vascular inflammation and plaque growth. There is also evidence that low and oscillatory shear stress contribute to arterial remodeling, entailing a loss in arterial elasticity and, therefore, an increased pulse-wave velocity. Although altered shear stress profiles, elasticity and inflammation are closely intertwined and critical for plaque growth, preclinical and clinical investigations for atherosclerosis mostly focus on the investigation of one of these parameters only due to the experimental limitations. However, cardiovascular magnetic resonance imaging (MRI) has been demonstrated to be a potent tool which can be used to provide insights into a large range of biological parameters in one experimental session. It enables the evaluation of the dynamic process of atherosclerotic lesion formation without the need for harmful radiation. Flow-sensitive MRI provides the assessment of hemodynamic parameters such as wall shear stress and pulse wave velocity which may replace invasive and radiation-based techniques for imaging of the vascular function and the characterization of early plaque development. In combination with inflammation imaging, the analyses and correlations of these parameters could not only significantly advance basic preclinical investigations of atherosclerotic lesion formation and progression, but also the diagnostic clinical evaluation for early identification of high-risk plaques, which are prone to rupture. In this review, we summarize the key applications of magnetic resonance imaging for the evaluation of plaque characteristics through flow sensitive and morphological measurements. The simultaneous measurements of functional and structural parameters will further preclinical research on atherosclerosis and has the potential to fundamentally improve the detection of inflammation and vulnerable plaques in patients.

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Section: Arterial Wall Stiffness In Atherosclerosismentioning

confidence: 99%

Evaluation of Plaque Characteristics and Inflammation Using Magnetic Resonance Imaging

et al. 2021

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“…erefore, more and more scholars use PPG waveforms to detect human-related physiological indexes, for example, blood pressure, blood sugar, blood oxygen saturation, heart, and other physiological indicators. Pulse wave analysis (PWA) is a typical method for studying AS, and it is widely used in both Chinese and Western medicine [10,11] [16]. Tjahjadi et al accurately classify blood pressure types based on two-way long and short-term memory and time-frequency analysis of PPG signals [17].…”

Section: Related Workmentioning

confidence: 99%

The Prediction of Atherosclerosis Index Based on Photoplethysmograph

Xie

Ren

et al. 2021

Scientific Programming

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Current atherosclerosis (AS) assessment devices have a disadvantage for users to carry around. In response to this shortcoming, we propose to collect the wrist photoplethysmograph (PPG) signal and create models to predict the indicators of atherosclerosis (cardiovascular age and right brachial and ankle pulse wave velocity (baPWV)). This study uses the maximum correlation coefficient method for feature selection and establishes multiple models to predict cardiovascular age and the right baPWV. The study results show that the prediction of cardiovascular age using the backpropagation (BP) neural network model is the best. Its Pearson correlation coefficient (PCC) is 0.9501 ( P < 0.05 ), and the model finds the best six physiological features as crest time (CT), crest time ratio (CTR), slop K, stiffness index (SI), reflection index (RI), and heart rate (HR). When predicting the right baPWV value on the right side, we propose a hybrid method MLR_BP, which has better experimental results than BP and MLR. The MLR_BP model improves the prediction accuracy, the predicted PCC value is 0.9204 ( P < 0.05 ), and the model only needs two features, HR and cardiovascular age. This study further verified the results of related literature and proved the relationship between AS and related physiological parameters. The proposed method is applied to wearable devices and has an application value for diagnosing AS and preventing cardiovascular diseases.

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“…In the previous adaptive PWI implementation, vessel wall homogeneity was evaluated based on the linearity of pulse wave propagation using the axial distension rate waveforms (v i (t)), and the homogeneity of peak axial distension rate (v i,peak ) [38] [39]. In this study, the following two criteria are used:…”

Section: Spatial Mechanical Inhomogeneity Detectionmentioning

confidence: 99%

Arterial wall mechanical inhomogeneity detection and atherosclerotic plaque characterization using high frame rate pulse wave imaging in carotid artery disease patients in vivo

Karageorgos

Apostolakis²,

Nauleau³

et al. 2020

Phys. Med. Biol.

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Pulse Wave Imaging (PWI) is a non-invasive, ultrasound-based technique, which provides information on arterial wall stiffness by estimating the Pulse Wave Velocity (PWV) along an imaged arterial wall segment. The aims of the present study were to: 1) utilize the PWI information to automatically and optimally divide the artery into the segments with most homogeneous properties and 2) assess the feasibility of this method to provide arterial wall mechanical characterization in normal and atherosclerotic carotid arteries in vivo. A silicone phantom consisting of a soft and stiff segment along its longitudinal axis was scanned at the stiffness transition, and the PWV in each segment was estimated through static testing. The proposed algorithm detected the stiffness interface with an average error of 0.98±0.49 mm and 1.04±0.27 mm in the soft-to-stiff and stiff-to-soft pulse wave transmission direction, respectively. Mean PWVs estimated in the case of the soft-to-stiff pulse wave transmission direction were 2.47±0.04m/sec and 3.43±0.08 m/sec for soft and stiff phantom segments, respectively, while in the case of stiff-to-soft transmission direction PWVs were 2.60±0.18 m/sec and 3.72±0.08m/sec for the soft and stiff phantom segments, respectively, which were in good agreement with the PWVs obtained through static testing (soft segment: 2.41 m/sec, stiff segment: 3.52 m/sec). Furthermore, the carotid arteries of N=9 young subjects (22-32 y.o.) and N=9 elderly subjects (60-73 y.o.) with no prior history of carotid artery disease were scanned, in vivo, as well as the atherosclerotic carotid arteries of N=12 (59-85 y.o.) carotid artery disease patients. One-way ANOVA with Holm-Sidak correction showed that the number of most homogeneous segments in which the artery was divided was significantly higher in the case of carotid artery disease patients compared to young (3.25±0.86 segments vs 1.00±0.00 segments, p-value<0.0001) and elderly non-atherosclerotic subjects (3.25±0.86 segments vs 1.44±0.51 segments p-value<0.0001), indicating increased wall inhomogeneity in atherosclerotic arteries. The compliance provided by the proposed algorithm was significantly higher in non-calcified/high-lipid plaques as compared

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

Cited by 22 publications

References 54 publications

Evaluation of Plaque Characteristics and Inflammation Using Magnetic Resonance Imaging

Evaluation of Plaque Characteristics and Inflammation Using Magnetic Resonance Imaging

The Prediction of Atherosclerosis Index Based on Photoplethysmograph

Arterial wall mechanical inhomogeneity detection and atherosclerotic plaque characterization using high frame rate pulse wave imaging in carotid artery disease patients in vivo

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