Assessment of Carotid Artery Plaque Components With Machine Learning Classification Using Homodyned-K Parametric Maps and Elastograms

Real-time carotid plaque recognition from dynamic ultrasound videos based on artificial neural network

PURPOSE

Carotid ultrasound allows noninvasive assessment of vascular anatomy and function with real-time display. Based on the transfer learning method, a series of research results have been obtained on the optimal image recognition and analysis of static images. However, for carotid plaque recognition, there are high requirements for self-developed algorithms in real-time ultrasound detection. This study aims to establish an automatic recognition system, Be Easy to Use (BETU), for the real-time and synchronous diagnosis of carotid plaque from ultrasound videos based on an artificial neural network.

MATERIALS AND METHODS

445 participants (mean age, 54.6±7.8 years; 227 men) were evaluated. Radiologists labeled a total of 3259 segmented ultrasound images from 445 videos with the diagnosis of carotid plaque, 2725 images were collected as a training dataset, and 554 images as a testing dataset. The automatic plaque recognition system BETU was established based on an artificial neural network, and remote application on a 5G environment was performed to test its diagnostic performance.

RESULTS

The diagnostic accuracy of BETU (98.5%) was consistent with the radiologist's (Kappa = 0.967, P < 0.001). Remote diagnostic feedback based on BETU-processed ultrasound videos could be obtained in 150ms across a distance of 1023 km between the ultrasound/BETU station and the consultation workstation.

CONCLUSION

Based on the good performance of BETU in real-time plaque recognition from ultrasound videos, 5G plus Artificial intelligence (AI)-assisted ultrasound real-time carotid plaque screening was achieved, and the diagnosis was made.

Multimodality quantitative ultrasound envelope statistics imaging based support vector machines for characterizing tissue scatterer distribution patterns: Methods and application in detecting microwave-induced thermal lesions

Ultrasound envelope statistics imaging, including ultrasound Nakagami imaging, homodyned-K imaging, and information entropy imaging, is an important group of quantitative ultrasound techniques for characterizing tissue scatterer distribution patterns, such as scatterer concentrations and arrangements. In this study, we proposed a machine learning approach to integrate the strength of multimodality quantitative ultrasound envelope statistics imaging techniques and applied it to detecting microwave ablation induced thermal lesions in porcine liver ex vivo. The quantitative ultrasound parameters included were homodyned-K α which is a scatterer clustering parameter related to the effective scatterer number per resolution cell, Nakagami m which is a shape parameter of the envelope probability density function, and Shannon entropy which is a measure of signal uncertainty or complexity. Specifically, the homodyned-K log10(α), Nakagami-m, and horizontally normalized Shannon entropy parameters were combined as input features to train a support vector machine (SVM) model to classify thermal lesions with higher scatterer concentrations from normal tissues with lower scatterer concentrations. Through heterogeneous phantom simulations based on Field II, the proposed SVM model showed a classification accuracy above 0.90; the area accuracy and Dice score of higher-scatterer-concentration zone identification exceeded 83% and 0.86, respectively, with the Hausdorff distance <26. Microwave ablation experiments of porcine liver ex vivo at 60-80 W, 1-3 min showed that the SVM model achieved a classification accuracy of 0.85; compared with single log10(α),m, or hNSE parametric imaging, the SVM model achieved the highest area accuracy (89.1%) and Dice score (0.77) as well as the smallest Hausdorff distance (46.38) of coagulation zone identification. We concluded that the proposed multimodality quantitative ultrasound envelope statistics imaging based SVM approach can enhance the capability to characterize tissue scatterer distribution patterns and has the potential to detect the thermal lesions induced by microwave ablation.

A deep learning algorithm to identify carotid plaques and assess their stability

Background

Carotid plaques are major risk factors for stroke. Carotid ultrasound can help to assess the risk and incidence rate of stroke. However, large-scale carotid artery screening is time-consuming and laborious, the diagnostic results inevitably involve the subjectivity of the diagnostician to a certain extent. Deep learning demonstrates the ability to solve the aforementioned challenges. Thus, we attempted to develop an automated algorithm to provide a more consistent and objective diagnostic method and to identify the presence and stability of carotid plaques using deep learning.

Methods

A total of 3,860 ultrasound images from 1,339 participants who underwent carotid plaque assessment between January 2021 and March 2023 at the Shanghai Eighth People's Hospital were divided into a 4:1 ratio for training and internal testing. The external test included 1,564 ultrasound images from 674 participants who underwent carotid plaque assessment between January 2022 and May 2023 at Xinhua Hospital affiliated with Dalian University. Deep learning algorithms, based on the fusion of a bilinear convolutional neural network with a residual neural network (BCNN-ResNet), were used for modeling to detect carotid plaques and assess plaque stability. We chose AUC as the main evaluation index, along with accuracy, sensitivity, and specificity as auxiliary evaluation indices.

Results

Modeling for detecting carotid plaques involved training and internal testing on 1,291 ultrasound images, with 617 images showing plaques and 674 without plaques. The external test comprised 470 ultrasound images, including 321 images with plaques and 149 without. Modeling for assessing plaque stability involved training and internal testing on 764 ultrasound images, consisting of 494 images with unstable plaques and 270 with stable plaques. The external test was composed of 279 ultrasound images, including 197 images with unstable plaques and 82 with stable plaques. For the task of identifying the presence of carotid plaques, our model achieved an AUC of 0.989 (95% CI: 0.840, 0.998) with a sensitivity of 93.2% and a specificity of 99.21% on the internal test. On the external test, the AUC was 0.951 (95% CI: 0.962, 0.939) with a sensitivity of 95.3% and a specificity of 82.24%. For the task of identifying the stability of carotid plaques, our model achieved an AUC of 0.896 (95% CI: 0.865, 0.922) on the internal test with a sensitivity of 81.63% and a specificity of 87.27%. On the external test, the AUC was 0.854 (95% CI: 0.889, 0.830) with a sensitivity of 68.52% and a specificity of 89.49%.

Conclusion

Deep learning using BCNN-ResNet algorithms based on routine ultrasound images could be useful for detecting carotid plaques and assessing plaque instability.

Resonance, Velocity, Dispersion, and Attenuation of Ultrasound-Induced Shear Wave Propagation in Blood Clot In Vitro Models

OBJECTIVE

Improve the characterization of mechanical properties of blood clots. Parameters derived from shear wave (SW) velocity and SW amplitude spectra were determined for gel phantoms and in vitro blood clots.

METHODS

Homogeneous phantoms and phantoms with gel or blood clot inclusions of different diameters and mechanical properties were analyzed. SW amplitude spectra were used to observe resonant peaks. Parameters derived from those resonant peaks were related to mimicked blood clot properties. Three regions of interest were tested to analyze where resonances occurred the most. For blood experiments, 20 samples from different pigs were analyzed over time during a 110-minute coagulation period using the Young modulus, SW frequency dispersion, and SW attenuation.

RESULTS

The mechanical resonance was manifested by an increase in the number of SW spectral peaks as the inclusion diameter was reduced (P < .001). In blood clot inclusions, the Young modulus increased over time during coagulation (P < .001). Descriptive spectral parameters (frequency peak, bandwidth, and distance between resonant peaks) were linearly correlated with clot elasticity values (P < .001) with R2  = .77 for the frequency peak, .60 for the bandwidth, and .48 for the distance between peaks. The SW dispersion and SW attenuation reflecting the viscous behavior of blood clots decreased over time (P < .001), mainly in the early stage of coagulation (first minutes).

CONCLUSION

The confined soft inclusion configuration favored SW mechanical resonances potentially challenging the computation of spectral-based parameters, such as the SW attenuation. The impact of resonances can be reduced by properly selecting the region of interest for data analysis.

Predictive stroke risk model with vision transformer-based Doppler features

BACKGROUND

Acute stroke is the leading cause of death and disability globally, with an estimated 16 million cases each year. The progression of carotid stenosis reduces blood flow to the intracranial vasculature, causing stroke. Early recognition of ischemic stroke is crucial for disease treatment and management.

PURPOSE

A computer-aided diagnosis (CAD) system was proposed in this study to rapidly evaluate ischemic stroke in carotid color Doppler (CCD).

METHODS

Based on the ground truth from the clinical examination report, the vision transformer (ViT) features extracted from all CCD images (513 stroke and 458 normal images) were combined in machine learning classifiers to generate the likelihood of ischemic stroke for each image. The pretrained weights from ImageNet reduced the time-consuming training process. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve were calculated to evaluate the stroke prediction model. The chi-square test, DeLong test, and Bonferroni correction for multiple comparisons were applied to deal with the type-I error. Only p values equal to or less than 0.00125 were considered to be statistically significant.

RESULTS

The proposed CAD system achieved an accuracy of 89%, a sensitivity of 94%, a specificity of 84%, and an area under the receiver operating characteristic curve of 0.95, outperforming the convolutional neural networks AlexNet (82%, p < 0.001), Inception-v3 (78%, p < 0.001), ResNet101 (84%, p < 0.001), and DenseNet201 (85%, p < 0.01). The computational time in model training was only 30 s, which would be efficient and practical in clinical use.

CONCLUSIONS

The experiment shows the promising use of CCD images in stroke estimation. Using the pretrained ViT architecture, the image features can be automatically and efficiently generated without human intervention. The proposed CAD system provides a rapid and reliable suggestion for diagnosing ischemic stroke.

Novel Imaging-Based Biomarkers for Identifying Carotid Plaque Vulnerability

Carotid artery disease has traditionally been assessed based on the degree of luminal narrowing. However, this approach, which solely relies on carotid stenosis, is currently being questioned with regard to modern risk stratification approaches. Recent guidelines have introduced the concept of the "vulnerable plaque," emphasizing specific features such as thin fibrous caps, large lipid cores, intraplaque hemorrhage, plaque rupture, macrophage infiltration, and neovascularization. In this context, imaging-based biomarkers have emerged as valuable tools for identifying higher-risk patients. Non-invasive imaging modalities and intravascular techniques, including ultrasound, computed tomography, magnetic resonance imaging, intravascular ultrasound, optical coherence tomography, and near-infrared spectroscopy, have played pivotal roles in characterizing and detecting unstable carotid plaques. The aim of this review is to provide an overview of the evolving understanding of carotid artery disease and highlight the significance of imaging techniques in assessing plaque vulnerability and informing clinical decision-making.

Comparing Focused-Tracked and Plane Wave-Tracked ARFI Log(VoA) In Silico and in Application to Human Carotid Atherosclerotic Plaque, Ex Vivo

A significant risk factor for ischemic stroke is carotid atherosclerotic plaque that is susceptible to rupture, with rupture potential conveyed by plaque morphology. Human carotid plaque composition and structure have been delineated noninvasively and in vivo by evaluating log(VoA), a parameter derived as the decadic log of the second time derivative of displacement induced by an acoustic radiation force impulse (ARFI). In prior work, ARFI-induced displacement was measured using conventional focused tracking; however, this requires a long data acquisition period, thereby reducing framerate. We herein evaluate if ARFI log(VoA) framerate can be increased without a reduction in plaque imaging performance using plane wave tracking instead. In silico, both focused- and plane wave-tracked log(VoA) decreased with increasing echobrightness, quantified as signal-to-noise ratio (SNR), but did not vary with material elasticity for SNRs below 40 dB. For SNRs of 40-60 dB, both focused- and plane wave-tracked log(VoA) varied with SNR and material elasticity. Above 60 dB SNR, both focused- and plane wave-tracked log(VoA) varied with material elasticity alone. This suggests that log(VoA) discriminates features according to a combination of their echobrightness and mechanical property. Further, while both focused- and plane-wave tracked log(VoA) values were artifactually inflated by mechanical reflections at inclusion boundaries, plane wave-tracked log(VoA) was more strongly impacted by off-axis scattering. Applied to three excised human cadaveric carotid plaques with spatially aligned histological validation, both log(VoA) methods detected regions of lipid, collagen, and calcium (CAL) deposits. These findings support that plane wave tracking performs comparably to focused tracking for log(VoA) imaging and that plane wave-tracked log(VoA) is a viable approach to discriminating clinically relevant atherosclerotic plaque features at a 30-fold higher framerate than by focused tracking.

Parallelized ultrasound homodyned-K imaging based on a generalized artificial neural network estimator

The homodyned-K (HK) distribution model is a generalized backscatter envelope statistical model for ultrasound tissue characterization, whose parameters are of physical meaning. To estimate the HK parameters is an inverse problem, and is quite complicated. Previously, we proposed an artificial neural network (ANN) estimator and an improved ANN (iANN) estimator for estimating the HK parameters, which are fast and flexible. However, a drawback of the conventional ANN and iANN estimators consists in that they use Monte Carlo simulations under known values of HK parameters to generate training samples, and thus the ANN and iANN models have to be re-trained when the size of the test sets (or of the envelope samples to be estimated) varies. In addition, conventional ultrasound HK imaging uses a sliding window technique, which is non-vectorized and does not support parallel computation, so HK image resolution is usually sacrificed to ensure a reasonable computation cost. To this end, we proposed a generalized ANN (gANN) estimator in this paper, which took the theoretical derivations of feature vectors for network training, and thus it is independent from the size of the test sets. Further, we proposed a parallelized HK imaging method that is based on the gANN estimator, which used a block-based parallel computation method, rather than the conventional sliding window technique. The gANN-based parallelized HK imaging method allowed a higher image resolution and a faster computation at the same time. Computer simulation experiments showed that the gANN estimator was generally comparable to the conventional ANN estimator in terms of HK parameter estimation performance. Clinical experiments of hepatic steatosis showed that the gANN-based parallelized HK imaging could be used to visually and quantitatively characterize hepatic steatosis, with similar performance to the conventional ANN-based HK imaging that used the sliding window technique, but the gANN-based parallelized HK imaging was over 3 times faster than the conventional ANN-based HK imaging. The parallelized computation method presented in this work can be easily extended to other quantitative ultrasound imaging applications.

BP-Net: Boundary and perfusion feature guided dual-modality ultrasound video analysis network for fibrous cap integrity assessment

Ultrasonography is one of the main imaging methods for monitoring and diagnosing atherosclerosis due to its non-invasiveness and low-cost. Automatic differentiation of carotid plaque fibrous cap integrity by using multi-modal ultrasound videos has significant diagnostic and prognostic value for cardiovascular and cerebrovascular disease patients. However, the task faces several challenges, including high variation in plaque location and shape, the absence of analysis mechanism focusing on fibrous cap, the lack of effective mechanism to capture the relevance among multi-modal data for feature fusion and selection, etc. To overcome these challenges, we propose a new target boundary and perfusion feature guided video analysis network (BP-Net) based on conventional B-mode ultrasound and contrast-enhanced ultrasound videos for assessing the integrity of fibrous cap. Based on our previously proposed plaque auto-tracking network, in our BP-Net, we further introduce the plaque edge attention module and reverse mechanism to focus the dual video analysis on the fiber cap of plaques. Moreover, to fully explore the rich information on the fibrous cap and inside/outside of the plaque, we propose a feature fusion module for B-mode and contrast video to filter out the most valuable features for fibrous cap integrity assessment. Finally, multi-head convolution attention is proposed and embedded into transformer-based network, which captures semantic features and global context information to obtain accurate evaluation of fibrous caps integrity. The experimental results demonstrate that the proposed method has high accuracy and generalizability with an accuracy of 92.35% and an AUC of 0.935, which outperforms than the state-of-the-art deep learning based methods. A series of comprehensive ablation studies suggest the effectiveness of each proposed component and show great potential in clinical application.

Ultrasound Homodyned-K Contrast-Weighted Summation Parametric Imaging Based on H-scan for Detecting Microwave Ablation Zones

The homodyned-K (HK) distribution is a generalized model of envelope statistics whose parameters α (the clustering parameter) and k (the coherent-to-diffuse signal ratio) can be used to monitor the thermal lesions. In this study, we proposed an ultrasound HK contrast-weighted summation (CWS) parametric imaging algorithm based on the H-scan technique and investigated the optimal window side length (WSL) of the HK parameters estimated by the XU estimator (an estimation method based on the first moment of the intensity and two log-moments, which was used in the proposed algorithm) through phantom simulations. H-scan diversified ultrasonic backscattered signals into low- and high-frequency passbands. After envelope detection and HK parameter estimation for each frequency band, the α and k parametric maps were obtained, respectively. According to the contrast between the target region and background, the (α or k) parametric maps of the dual-frequency band were weighted and summed, and then the CWS images were yielded by pseudo-color imaging. The proposed HK CWS parametric imaging algorithm was used to detect the microwave ablation coagulation zones of porcine liver ex vivo under different powers and treatment durations. The performance of the proposed algorithm was compared with that of the conventional HK parametric imaging and frequency diversity and compounding Nakagami imaging algorithms. For two-dimensional HK parametric imaging, it was found that a WSL equal to 4 pulse lengths of the transducer was sufficient for estimating the α and k parameters in terms of both parameter estimation stability and parametric imaging resolution. The HK CWS parametric imaging provided an improved contrast-to-noise ratio over conventional HK parametric imaging, and the HK α_cws parametric imaging achieved the best accuracy and Dice score of coagulation zone detection.

The Kidneys Are Not All Normal: Transplanted Kidneys and Their Speckle Distributions

OBJECTIVE

Modelling ultrasound speckle to characterise tissue properties has generated considerable interest. As speckle is dependent on the underlying tissue architecture, modelling it may aid in tasks such as segmentation or disease detection. For the transplanted kidney, where ultrasound is used to investigate dysfunction, it is unknown which statistical distribution best characterises such speckle. This applies to the regions of the transplanted kidney: the cortex, the medulla and the central echogenic complex. Furthermore, it is unclear how these distributions vary by patient variables such as age, sex, body mass index, primary disease or donor type. These traits may influence speckle modelling given their influence on kidney anatomy. We investigate these two aims.

METHODS

B-mode images from n = 821 kidney transplant recipients (one image per recipient) were automatically segmented into the cortex, medulla and central echogenic complex using a neural network. Seven distinct probability distributions were fitted to each region's histogram, and statistical analysis was performed.

DISCUSSION

The Rayleigh and Nakagami distributions had model parameters that differed significantly between the three regions (p ≤ 0.05). Although both had excellent goodness of fit, the Nakagami had higher Kullbeck-Leibler divergence. Recipient age correlated weakly with scale in the cortex (Ω: ρ = 0.11, p = 0.004), while body mass index correlated weakly with shape in the medulla (m: ρ = 0.08, p = 0.04). Neither sex, primary disease nor donor type exhibited any correlation.

CONCLUSION

We propose the Nakagami distribution be used to characterize transplanted kidneys regionally independent of disease etiology and most patient characteristics.

A Study on a Parameter Estimator for the Homodyned K Distribution Based on Table Search for Ultrasound Tissue Characterization

OBJECTIVE

The homodyned K (HK) distribution is considered to be the most suitable distribution in the context of tissue characterization; therefore, the search for a rapid and reliable parameter estimator for HK distribution is important.

METHODS

We propose a novel parameter estimator based on a table search (TS) for HK parameter estimates. The TS estimator can inherit the strength of conventional estimators by integrating various features and taking advantage of the TS method in a rapid and easy operation. Performance of the proposed TS estimator was evaluated and compared with that of XU (the estimation method based on X and U statistics) and artificial neural network (ANN) estimators.

DISCUSSION

The simulation results revealed that the TS estimator is superior to the XU and ANN estimators in terms of normalized standard deviations and relative root mean squared errors of parameter estimation, and is faster. Clinical experiments found that the area under the receiver operating curve for breast lesion classification using the parameters estimated by the TS estimator could reach 0.871.

CONCLUSION

The proposed TS estimator is more accurate, reliable and faster than the state-of-the-art XU and ANN estimators and has great potential for ultrasound tissue characterization based on the HK distribution.

Spectral analysis of ultrasound backscatter for non-invasive measurement of plaque composition

Carotid atherosclerotic plaque composition may be an important indication of patient risk for future cerebrovascular events. Ultrasound spectral analysis has the potential to provide a robust measure of plaque composition in vivo if the backscatter transfer function can be sufficiently isolated from the effects of attenuation from overlying tissue, receive and transmit transfer functions from the ultrasound system and transducer, and diffraction. This study examines the usefulness of the nonlinearly generated second harmonic portion of the backscatter signal and the effects of a variety of attenuation compensation techniques for noninvasively characterizing human carotid plaque using spectral analysis and machine learning. Post-beamformed ultrasound backscatter radiofrequency (RF) data were acquired from 6 normal subjects and 119 carotid endarterectomy patients prior to surgery. Plaque obtained following surgery was histologically processed, and regions of interest (ROI) corresponding to homogenous tissue types (fibrous/fibro-lipidic, hemorrhagic and/or necrotic core and calcified) were selected from RF data. Both the harmonic and fundamental power spectra for each ROI was obtained and normalized by data from a uniform phantom (0.5 dB/cm-MHz slope of attenuation). Additional attenuation compensation approaches were compared to simply using the reference phantom: (1) optimum power spectral shift estimation, (2) one-step adventitial, or (3) two-step adventitial. Spectral parameters extracted from both the fundamental and harmonic estimates of the backscatter transfer function of 363 ROI's from 152 plaque specimens were used to train and test random forest and support vector machine classification models. The best results came from using spectral parameters derived from both the fundamental and second harmonic bands with a predictive accuracy of 65-68%, kappa statistic of 0.49-0.54, and accuracies of 84% for fibrous, 68-74% for hemorrhagic and/or necrotic core, and 78-81% for calcified ROI's. The result indicated that the nonlinearly generated second harmonic portion of backscatter is useful for carotid plaque tissue characterization and that a reference phantom approach with a 0.5 dB/cm-MHz slope of attenuation works as well as more complicated approaches.

HRU-Net: A Transfer Learning Method for Carotid Artery Plaque Segmentation in Ultrasound Images

Carotid artery stenotic plaque segmentation in ultrasound images is a crucial means for the analysis of plaque components and vulnerability. However, segmentation of severe stenotic plaques remains a challenging task because of the heterogeneities of inter-plaques and intra-plaques, and obscure boundaries of plaques. In this paper, we propose an automated HRU-Net transfer learning method for segmenting carotid plaques, using the limited images. The HRU-Net is based on the U-Net encoder−decoder paradigm, and cross-domain knowledge is transferred for plaque segmentation by fine-tuning the pretrained ResNet-50. Moreover, a cropped-blood-vessel image augmentation is customized for the plaque position constraint during training only. Moreover, hybrid atrous convolutions (HACs) are designed to derive diverse long-range dependences for refined plaque segmentation that are used on high-level semantic layers to exploit the implicit discrimination features. The experiments are performed on 115 images; Firstly, the 10-fold cross-validation, using 40 images with severe stenosis plaques, shows that the proposed method outperforms some of the state-of-the-art CNN-based methods on Dice, IoU, Acc, and modified Hausdorff distance (MHD) metrics; the improvements on metrics of Dice and MHD are statistically significant (p < 0.05). Furthermore, our HRU-Net transfer learning method shows fine generalization performance on 75 new images with varying degrees of plaque stenosis, and it may be used as an alternative for automatic noisy plaque segmentation in carotid ultrasound images clinically.

Liver Fibrosis Assessment Using Radiomics of Ultrasound Homodyned-K imaging Based on the Artificial Neural Network Estimator

The homodyned-K distribution is an important ultrasound backscatter envelope statistics model of physical meaning, and the parametric imaging of the model parameters has been explored for quantitative ultrasound tissue characterization. In this paper, we proposed a new method for liver fibrosis characterization by using radiomics of ultrasound backscatter homodyned-K imaging based on an improved artificial neural network (iANN) estimator. The iANN estimator was used to estimate the ultrasound homodyned-K distribution parameters k and α from the backscattered radiofrequency (RF) signals of clinical liver fibrosis (n = 237), collected with a 3-MHz convex array transducer. The RF data were divided into two groups: Group I corresponded to liver fibrosis with no hepatic steatosis (n = 94), and Group II corresponded to liver fibrosis with mild to severe hepatic steatosis (n = 143). The estimated homodyned-K parameter values were then used to construct k and α parametric images using the sliding window technique. Radiomics features of k and α parametric images were extracted, and feature selection was conducted. Logistic regression classification models based on the selected radiomics features were built for staging liver fibrosis. Experimental results showed that the proposed method is overall superior to the radiomics method of uncompressed envelope images when assessing liver fibrosis. Regardless of hepatic steatosis, the proposed method achieved the best performance in staging liver fibrosis ≥F1, ≥F4, and the area under the receiver operating characteristic curve was 0.88, 0.85 (Group I), and 0.85, 0.86 (Group II), respectively. Radiomics has improved the ability of ultrasound backscatter statistical parametric imaging to assess liver fibrosis, and is expected to become a new quantitative ultrasound method for liver fibrosis characterization.

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

BACKGROUND

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 ± 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.

Machine learning-enabled quantitative ultrasound techniques for tissue differentiation

PURPOSE

Quantitative ultrasound (QUS) infers properties about tissue microstructure from backscattered radio-frequency ultrasound data. This paper describes how to implement the most practical QUS parameters using an ultrasound research system for tissue differentiation.

METHODS

This study first validated chicken liver and gizzard muscle as suitable acoustic phantoms for human brain and brain tumour tissues via measurement of the speed of sound and acoustic attenuation. A total of thirteen QUS parameters were estimated from twelve samples, each using data obtained with a transducer with a frequency of 5-11 MHz. Spectral parameters, i.e., effective scatterer diameter and acoustic concentration, were calculated from the backscattered power spectrum of the tissue, and echo envelope statistics were estimated by modelling the scattering inside the tissue as a homodyned K-distribution, yielding the scatterer clustering parameter α and the structure parameter κ. Standard deviation and higher-order moments were calculated from the echogenicity value assigned in conventional B-mode images.

RESULTS

The k-nearest neighbours algorithm was used to combine those parameters, which achieved 94.5% accuracy and 0.933 F1-score.

CONCLUSION

We were able to generate classification parametric images in near-real-time speed as a potential diagnostic tool in the operating room for the possible use for human brain tissue characterisation.

Shear Wave Elastography and Quantitative Ultrasound as Biomarkers to Characterize Deep Vein Thrombosis In Vivo

OBJECTIVE

Investigate shear wave elastography (SWE) and quantitative ultrasound (QUS) parameters in patients hospitalized for lower limb deep vein thrombosis (DVT).

METHOD

Sixteen patients with DVT were recruited and underwent SWE and radiofrequency data acquisitions for QUS on day 0, day 7, and day 30 after the beginning of symptoms, in both proximal and distal zones of the clot identified on B-mode scan. SWE and QUS features were computed to differentiate between thrombi at day 0, day 7, and day 30 following treatment with heparin or oral anticoagulant. The Young's modulus from SWE was computed, as well as QUS homodyned K-distribution (HKD) parameters reflecting blood clot structure. Median and interquartile range of SWE and QUS parameters within clot were taken as features.

RESULTS

In the proximal zone of the clot, the HKD ratio of coherent-to-diffuse backscatter median showed a significant decrease from day 7 to day 30 (P = .036), while the HKD ratio of diffuse-to-total backscatter median presented a significant increase from day 7 to day 30 (P = .0491). In the distal zone of the clot, the HKD normalized intensity of the echo envelope median showed a significant increase from day 0 to day 30 (P = .0062). No SWE features showed statistically significant differences over time. Nonetheless, a trend of lower median of Young's modulus within clot for patients who developed a pulmonary embolism was observed.

CONCLUSION

QUS features may be relevant to characterize clot's evolution over time. Further analysis of their clinical interpretation and validation on a larger dataset would deserve to be studied.

Artificial Intelligence in Quantitative Ultrasound Imaging: A Survey

Quantitative ultrasound (QUS) imaging is a safe, reliable, inexpensive, and real-time technique to extract physically descriptive parameters for assessing pathologies. Compared with other major imaging modalities such as computed tomography and magnetic resonance imaging, QUS suffers from several major drawbacks: poor image quality and inter- and intra-observer variability. Therefore, there is a great need to develop automated methods to improve the image quality of QUS. In recent years, there has been increasing interest in artificial intelligence (AI) applications in medical imaging, and a large number of research studies in AI in QUS have been conducted. The purpose of this review is to describe and categorize recent research into AI applications in QUS. We first introduce the AI workflow and then discuss the various AI applications in QUS. Finally, challenges and future potential AI applications in QUS are discussed.

Recent advances in vascular ultrasound imaging technology and their clinical implications

In this paper recent advances in vascular ultrasound imaging technology are discussed, including three-dimensional ultrasound (3DUS), contrast-enhanced ultrasound (CEUS) and strain- (SE) and shear-wave-elastography (SWE). 3DUS imaging allows visualisation of the actual 3D anatomy and more recently of flow, and assessment of geometrical, morphological and mechanical features in the carotid artery and the aorta. CEUS involves the use of microbubble contrast agents to estimate sensitive blood flow and neovascularisation (formation of new microvessels). Recent developments include the implementation of computerised tools for automated analysis and quantification of CEUS images, and the possibility to measure blood flow velocity in the aorta. SE, which yields anatomical maps of tissue strain, is increasingly being used to investigate the vulnerability of the carotid plaque, but is also promising for the coronary artery and the aorta. SWE relies on the generation of a shear wave by remote acoustic palpation and its acquisition by ultrafast imaging, and is useful for measuring arterial stiffness. Such advances in vascular ultrasound technology, with appropriate validation in clinical trials, could positively change current management of patients with vascular disease, and improve stratification of cardiovascular risk.

Performance Analysis of Machine Learning and Deep Learning Architectures on Early Stroke Detection Using Carotid Artery Ultrasound Images

Atherosclerotic plaque deposit in the carotid artery is used as an early estimate to identify the presence of cardiovascular diseases. Ultrasound images of the carotid artery are used to provide the extent of stenosis by examining the intima-media thickness and plaque diameter. A total of 361 images were classified using machine learning and deep learning approaches to recognize whether the person is symptomatic or asymptomatic. CART decision tree, random forest, and logistic regression machine learning algorithms, convolutional neural network (CNN), Mobilenet, and Capsulenet deep learning algorithms were applied in 202 normal images and 159 images with carotid plaque. Random forest provided a competitive accuracy of 91.41% and Capsulenet transfer learning approach gave 96.7% accuracy in classifying the carotid artery ultrasound image database.

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.

Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications

Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE^® and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.

Parameter estimation of the homodyned K distribution based on an artificial neural network for ultrasound tissue characterization

The homodyned K (HK) distribution allows a general description of ultrasound backscatter envelope statistics with specific physical meanings. In this study, we proposed a new artificial neural network (ANN) based parameter estimation method of the HK distribution. The proposed ANN estimator took advantages of ANNs in learning and function approximation and inherited the strengths of conventional estimators through extracting five feature parameters from backscatter envelope signals as the input of the ANN: the signal-to-noise ratio (SNR), skewness, kurtosis, as well as X- and U-statistics. Computer simulations and clinical data of hepatic steatosis were used for validations of the proposed ANN estimator. The ANN estimator was compared with the RSK (the level-curve method that uses SNR, skewness, and kurtosis based on the fractional moments of the envelope) and XU (the estimation method based on X- and U-statistics) estimators. Computer simulation results showed that the relative bias was best for the XU estimator, whilst the normalized standard deviation was overall best for the ANN estimator. The ANN estimator was almost one order of magnitude faster than the RSK and XU estimators. The ANN estimator also yielded comparable diagnostic performance to state-of-the-art HK estimators in the assessment of hepatic steatosis. The proposed ANN estimator has great potential in ultrasound tissue characterization based on the HK distribution.

Monitoring microwave ablation using ultrasound homodyned K imaging based on the noise-assisted correlation algorithm: An ex vivo study

In this paper, we proposed ultrasound homodyned K (HK) imaging based on the noise-assisted correlation algorithm (NCA) for monitoring microwave ablation of porcine liver ex vivo. The NCA-based HK (α_NCA and k_NCA) imaging was compared with NCA-based Nakagami (m_NCA) imaging and NCA-based cumulative echo decorrelation (CED_NCA) imaging. Backscattered ultrasound radiofrequency signals of porcine liver ex vivo during and after the heating of microwave ablation were collected (n = 15), which were processed for constructing B-mode imaging, NCA-based HK imaging, NCA-based Nakagami imaging, and NCA-based CED imaging. To quantitatively evaluate the final coagulation zone, the polynomial approximation (PAX) technique was applied. The accuracy of detecting coagulation area with α_NCA, k_NCA, m_NCA, and CED_NCA parametric imaging was evaluated by comparing the PAX imaging with the gross pathology. The receiver operating characteristic (ROC) curve was used to further evaluate the performance of the three quantitative ultrasound imaging methods for detecting the coagulation zone. Experimental results showed that the average accuracies of α_NCA, k_NCA, m_NCA, and CED_NCA parametric imaging combined with PAX imaging were 89.6%, 83.25%, 89.23%, and 91.6%, respectively. The average areas under the ROC curve (AUROCs) of α_NCA, k_NCA, m_NCA, and CED_NCA parametric imaging were 0.83, 0.77, 0.83, and 0.86, respectively. The proposed NCA-based HK imaging may be used as a new method for monitoring microwave ablation.

Carotid Plaque Fibrous Cap Thickness Measurement by ARFI Variance of Acceleration: In Vivo Human Results

This study evaluates the performance of an acoustic radiation force impulse (ARFI)-based outcome parameter, the decadic logarithm of the variance of acceleration, or log(VoA), for measuring carotid fibrous cap thickness. Carotid plaque fibrous cap thickness measurement by log(VoA) was compared to that by ARFI peak displacement (PD) in patients undergoing clinically indicated carotid endarterectomy using a spatially-matched histological validation standard. Fibrous caps in parametric log(VoA) and PD images were automatically segmented using a custom clustering algorithm, and a pathologist with expertise in atherosclerosis hand-delineated fibrous caps in histology. Over 10 fibrous caps, log(VoA)-derived thickness was more strongly correlated to histological thickness than PD-derived thickness, with Pearson correlation values of 0.98 for log(VoA) compared to 0.89 for PD. The log(VoA)-derived cap thickness also had better agreement with histology-measured thickness, as assessed by the concordance correlation coefficient (0.95 versus 0.62), and, by Bland-Altman analysis, was more consistent than PD-derived fibrous cap thickness. These results suggest that ARFI log(VoA) enables improved discrimination of fibrous cap thickness relative to ARFI PD and further contributes to the growing body of evidence demonstrating ARFI's overall relevance to delineating the structure and composition of carotid atherosclerotic plaque for stroke risk prediction.

Imaging of the vulnerable carotid plaque: Role of imaging techniques and a research agenda

OBJECTIVES

Atherothrombosis in the carotid arteries is a main cause of ischemic stroke and may depend on plaque propensity to complicate with rupture or erosion, in turn related to vulnerability features amenable to in vivo imaging. This would provide an opportunity for risk stratification and-potentially-local treatment of more vulnerable plaques. We here review current information on this topic.

METHODS

We systematically reviewed the literature for concepts derived from pathophysiologic, histopathologic, and clinical studies on imaging techniques attempting at identifying vulnerable carotid lesions.

RESULTS

Ultrasound, MRI, CT, and nuclear medicine-based techniques, alone or with multimodality approaches, all have a link to pathophysiology and describe different-potentially complementary-aspects of lesions prone to complications. There is also, however, a true paucity of head-to-head comparisons of such techniques for practical implementation of a thorough and cost-effective diagnostic strategy based on evaluation of outcomes. Especially in asymptomatic patients, major international societies leave wide margins of indecision in the advice to techniques guiding interventions to prevent atherothrombotic stroke.

CONCLUSIONS

To improve practical management of such patients-in addition to the patient's vulnerability for systemic reasons-a more precise identification of the vulnerable plaque is needed. A better definition of the diagnostic yield of each imaging approach in comparison with the others should be pursued for a cost-effective translation of the single techniques. Practical translation to guide future clinical practice should be based on improved knowledge of the specific pathophysiologic correlates and on a comparative modality approach, linked to subsequent stroke outcomes.

Three-dimensional ultrasound evaluation of the effects of pomegranate therapy on carotid plaque texture using locality preserving projection

BACKGROUND AND OBJECTIVE

Dietary supplements are expected to confer a smaller beneficial effect than medical treatments. Therefore, there is a need to develop cost-effective biomarkers that can demonstrate the efficacy of such supplements for carotid atherosclerosis. The aim of this study is to develop such a biomarker based on the changes of 376 plaque textural features measured from 3D ultrasound images.

METHODS

Since the number of features (376) was greater than the number of subjects (171) in this study, principal component analysis (PCA) was applied to reduce the dimensionality of feature vectors. To generate a scalar biomarker for each subject, elements in the reduced feature vectors produced by PCA were weighted using locality preserving projections (LPP) to capture essential patterns exhibited locally in the feature space. 96 subjects treated by pomegranate juice and tablets, and 75 subjects receiving placebo-matching juice and tablets were evaluated in this study. The discriminative power of the proposed biomarker was evaluated and compared with existing biomarkers using t-tests. As the cost of a clinical trial is directly related to the number of subjects enrolled, the cost-effectiveness of the proposed biomarker was evaluated by sample size estimation.

RESULTS

The proposed biomarker was more able to discriminate plaque changes exhibited by the pomegranate and placebo groups than total plaque volume (TPV) according to the result of t-tests (TPV: p=0.34, Proposed biomarker: p=1.5×10^-5). The sample size required by the new biomarker to detect a significant effect was 20 times smaller than that required by TPV.

CONCLUSION

With the increase in cost-effectiveness afforded by the proposed biomarker, more proof-of-principle studies for novel treatment options could be performed.

Value of homodyned K distribution in ultrasound parametric imaging of hepatic steatosis: An animal study

Ultrasound is the first-line tool for screening hepatic steatosis. Statistical distributions can be used to model the backscattered signals for liver characterization. The Nakagami distribution is the most frequently adopted model; however, the homodyned K (HK) distribution has received attention due to its link to physical meaning and improved parameter estimation through X- and U-statistics (termed "XU"). To assess hepatic steatosis, we proposed HK parametric imaging based on the α parameter (a measure of the number of scatterers per resolution cell) calculated using the XU estimator. Using a commercial system equipped with a 7-MHz linear array transducer, phantom experiments were performed to suggest an appropriate window size for α imaging using the sliding window technique, which was further applied to measuring the livers of rats (n = 66) with hepatic steatosis induced by feeding the rats a methionine- and choline-deficient diet. The relationships between the α parameter, the stage of hepatic steatosis, and histological features were verified by the correlation coefficient r, one-way analysis of variance, and regression analysis. The phantom results showed that the window side length corresponding to five times the pulse length supported a reliable α imaging. The α parameter showed a promising performance for grading hepatic steatosis (p < 0.05; r² = 0.68). Compared with conventional Nakagami imaging, α parametric imaging provided significant information associated with fat droplet size (p < 0.05; r² = 0.53), enabling further analysis and evaluation of severe hepatic steatosis.

Hepatic steatosis assessment using ultrasound homodyned-K parametric imaging: the effects of estimators

BACKGROUND

The homodyned-K (HK) distribution is an important statistical model for describing ultrasound backscatter envelope statistics. HK parametric imaging has shown potential for characterizing hepatic steatosis. However, the feasibility of HK parametric imaging in assessing human hepatic steatosis in vivo remains unclear.

METHODS

In this paper, ultrasound HK μ parametric imaging was proposed for assessing human hepatic steatosis in vivo. Two recent estimators for the HK model, RSK (the level-curve method that uses the signal-to-noise ratio (SNR), skewness, and kurtosis based on the fractional moments of the envelope) and XU (the estimation method based on the first moment of the intensity and two log-moments, namely X- and U-statistics), were investigated. Liver donors (n=72) and patients (n=204) were recruited to evaluate hepatic fat fractions (HFFs) using magnetic resonance spectroscopy and to evaluate the stages of fatty liver disease (normal, mild, moderate, and severe) using liver biopsy with histopathology. Livers were scanned using a 3-MHz ultrasound to construct μ _RSK and μ _XU images to correlate with HFF analyses and fatty liver stages. The μ _RSK and μ _XU parametric images were constructed using the sliding window technique with the window side length (WSL) =1-9 pulse lengths (PLs). The diagnostic values of the μ _RSK and μ _XU parametric imaging methods were evaluated using receiver operating characteristic (ROC) curves.

RESULTS

For the 72 participants in Group A, the μ _RSK parametric imaging with WSL =2-9 PLs exhibited similar correlation with log₁₀(HFF), and the μ _RSK parametric imaging with WSL = 3 PLs had the highest correlation with log₁₀(HFF) (r=0.592); the μ _XU parametric imaging with WSL =1-9 PLs exhibited similar correlation with log₁₀(HFF), and the μ _XU parametric imaging with WSL =1 PL had the highest correlation with log₁₀(HFF) (r=0.628). For the 204 patients in Group B, the areas under the ROC (AUROCs) obtained using μ _RSK for fatty stages ≥ mild (AUROC₁), ≥ moderate (AUROC₂), and ≥ severe (AUROC₃) were (AUROC₁, AUROC₂, AUROC₃) = (0.56, 0.57, 0.53), (0.68, 0.72, 0.75), (0.73, 0.78, 0.80), (0.74, 0.77, 0.79), (0.74, 0.78, 0.79), (0.75, 0.80, 0.82), (0.74, 0.77, 0.83), (0.74, 0.78, 0.84) and (0.73, 0.76, 0.83) for WSL =1, 2, 3, 4, 5, 6, 7, 8 and 9 PLs, respectively. The AUROCs obtained using μ _XU for fatty stages ≥ mild, ≥ moderate, and ≥ severe were (AUROC₁, AUROC₂, AUROC₃) = (0.75, 0.83, 0.81), (0.74, 0.80, 0.80), (0.76, 0.82, 0.82), (0.74, 0.80, 0.84), (0.76, 0.80, 0.83), (0.75, 0.80, 0.84), (0.75, 0.79, 0.85), (0.75, 0.80, 0.85) and (0.73, 0.77, 0.83) for WSL = 1, 2, 3, 4, 5, 6, 7, 8 and 9 PLs, respectively.

CONCLUSIONS

Both the μ _RSK and μ _XU parametric images are feasible for evaluating human hepatic steatosis. The WSL exhibits little impact on the diagnosing performance of the μ _RSK and μ _XU parametric imaging. The μ _XU parametric imaging provided improved performance compared to the μ _RSK parametric imaging in characterizing human hepatic steatosis in vivo.

Assessment of Offshore Wind Characteristics and Wind Energy Potential in Bohai Bay, China

Wind energy, one of the most sustainable renewable energy sources, has been extensively developed worldwide. However, owing to the strong regional and seasonal differences, it is necessary to first evaluate wind energy resources in detail. In this study, the offshore wind characteristics and wind energy potential of Bohai Bay (38.7° N, 118.7° E), China, were statistically analyzed using two-year offshore wind data with a time interval of one second. Furthermore, Nakagami and Rician distributions were used for wind energy resource assessment. The results show that the main wind direction in Bohai Bay is from the east (−15°–45°), with a speed below 12 m/s, mainly ranging from 4 to 8 m/s. The main wind speed ranges in April and October are higher than those in August and December. The night wind speed is generally higher than that in the daytime. The Nakagami and Rician distributions performed reasonably in calculating the wind speed distributions and potential assessments. However, Nakagami distribution provided better wind resource assessment in this region. The wind potential assessment results suggest that Bohai Bay could be considered as a wind class I region, with east as the dominant wind direction.

Delineation of Human Carotid Plaque Features In Vivo by Exploiting Displacement Variance

While in vivo acoustic radiation force impulse (ARFI)-induced peak displacement (PD) has been demonstrated to have high sensitivity and specificity for differentiating soft from stiff plaque components in patients with carotid plaque, the parameter exhibits poorer performance for distinguishing between plaque features with similar stiffness. To improve discrimination of carotid plaque features relative to PD, we hypothesize that signal correlation and signal-to-noise ratio (SNR) can be combined, outright or via displacement variance. Plaque feature detection by displacement variance, evaluated as the decadic logarithm of the variance of acceleration and termed "log(VoA)," was compared to that achieved by exploiting SNR, cross correlation coefficient, and ARFI-induced PD outcome metrics. Parametric images were rendered for 25 patients undergoing carotid endarterectomy, with spatially matched histology confirming plaque composition and structure. On average, across all plaques, log(VoA) was the only outcome metric with values that statistically differed between regions of lipid-rich necrotic core (LRNC), intraplaque hemorrhage (IPH), collagen (COL), and calcium (CAL). Further, log(VoA) achieved the highest contrast-to-noise ratio (CNR) for discriminating between LRNC and IPH, COL and CAL, and grouped soft (LRNC and IPH) and stiff (COL and CAL) plaque components. More specifically, relative to the previously demonstrated ARFI PD parameter, log(VoA) achieved 73% higher CNR between LRNC and IPH and 59% higher CNR between COL and CAL. These results suggest that log(VoA) enhances the differentiation of LRNC, IPH, COL, and CAL in human carotid plaques, in vivo, which is clinically relevant to improving stroke risk prediction and medical management.

Interoperator Reproducibility of Carotid Elastography for Identification of Vulnerable Atherosclerotic Plaques

Ultrasound-based carotid elastography has been developed to evaluate the vulnerability of carotid atherosclerotic plaques. The aim of this study was to investigate the in vivo interoperator reproducibility of carotid elastography for the identification of vulnerable plaques, with high-resolution magnetic resonance imaging (MRI) as reference. Ultrasound radio-frequency data of 45 carotid arteries (including 53 plaques) from 32 volunteers were acquired separately by two experienced operators in the longitudinal view and then were used to estimate the interframe axial strain rate (ASR) with a two-step optical flow method. The maximum 99th percentile of absolute ASR of all plaques in a carotid artery was used as the elastographic index. MRI scanning was also performed on each volunteer to identify the vulnerable plaque. The results showed no systematic bias in the Bland-Altman plot and an intraclass correlation coefficient of 0.66 between the two operators. In addition, no statistical significance was found between the receiver operating characteristic (ROC) curves from the two operators ( ), and their areas under the ROC curves were 0.83 and 0.77, respectively. Using the mean measurements of the two operators as the classification criterion, a sensitivity of 71.4%, a specificity of 87.1%, and an accuracy of 82.2% were obtained with a cutoff value of 1.37 [Formula: see text]. This study validates the interoperator reproducibility of ultrasound-based carotid elastography for identifying vulnerable carotid plaques.

Hepatic Steatosis Assessment Using Quantitative Ultrasound Parametric Imaging Based on Backscatter Envelope Statistics

Hepatic steatosis is a key manifestation of non-alcoholic fatty liver disease (NAFLD). Early detection of hepatic steatosis is of critical importance. Currently, liver biopsy is the clinical golden standard for hepatic steatosis assessment. However, liver biopsy is invasive and associated with sampling errors. Ultrasound has been recommended as a first-line diagnostic test for the management of NAFLD. However, B-mode ultrasound is qualitative and can be affected by factors including image post-processing parameters. Quantitative ultrasound (QUS) aims to extract quantified acoustic parameters from the ultrasound backscattered signals for ultrasound tissue characterization and can be a complement to conventional B-mode ultrasound. QUS envelope statistics techniques, both statistical model-based and non-model-based, have shown potential for hepatic steatosis characterization. However, a state-of-the-art review of hepatic steatosis assessment using envelope statistics techniques is still lacking. In this paper, envelope statistics-based QUS parametric imaging techniques for characterizing hepatic steatosis are reviewed and discussed. The reviewed ultrasound envelope statistics parametric imaging techniques include acoustic structure quantification imaging, ultrasound Nakagami imaging, homodyned-K imaging, kurtosis imaging, and entropy imaging. Future developments are suggested.

Lipoproteins in Cardiovascular Calcification: Potential Targets and Challenges

Previously considered a degenerative process, cardiovascular calcification is now established as an active process that is regulated in several ways by lipids, phospholipids, and lipoproteins. These compounds serve many of the same functions in vascular and valvular calcification as they do in skeletal bone calcification. Hyperlipidemia leads to accumulation of lipoproteins in the subendothelial space of cardiovascular tissues, which leads to formation of mildly oxidized phospholipids, which are known bioactive factors in vascular cell calcification. One lipoprotein of particular interest is Lp(a), which showed genome-wide significance for the presence of aortic valve calcification and stenosis. It carries an important enzyme, autotaxin, which produces lysophosphatidic acid (LPA), and thus has a key role in inflammation among other functions. Matrix vesicles, extruded from the plasma membrane of cells, are the sites of initiation of mineral formation. Phosphatidylserine, a phospholipid in the membranes of matrix vesicles, is believed to complex with calcium and phosphate ions, creating a nidus for hydroxyapatite crystal formation in cardiovascular as well as in skeletal bone mineralization. This review focuses on the contributions of lipids, phospholipids, lipoproteins, and autotaxin in cardiovascular calcification, and discusses possible therapeutic targets.