Carotid artery ultrasound image analysis: A review of the literature

Association of the High-Sensitivity C-Reactive Protein-to-Albumin Ratio with Carotid Atherosclerotic Plaque: A Community-Based Cohort Study

Background

The inflammatory response is a pivotal factor in accelerating the progression of atherosclerosis. The high-sensitivity C-reactive protein-to-albumin ratio (CAR) has emerged as a novel marker of systemic inflammation. However, few studies have shown the CAR to be a promising prognostic marker for carotid atherosclerotic disease. This study aimed to analyse the predictive role of the CAR in carotid atherosclerotic disease.

Methods

This community-based cohort study recruited 2003 participants from the Rose asymptomatic IntraCranial Artery Stenosis (RICAS) study who were free of stroke or transient ischemic attack. Carotid atherosclerotic plaques and their stability were identified via carotid ultrasound. Logistic regression models were utilized to investigate the association between CAR and the presence of carotid atherosclerotic plaques.

Results

The prevalence of carotid atherosclerotic plaques was 38.79% in this study. After adjusting for clinical risk factors, including sex, age, dyslipidemia, hypertension, diabetes mellitus (DM), and smoking and drinking habits, a high CAR-level was independently associated with carotid plaque (odds ratio [OR] of upper: 1.46, 95% confidence interval [CI]: 1.13-1.90, P = 0.004; P for trend = 0.011). The highest CAR tertile was still significantly associated with carotid plaques among middle-aged (40-64 years) or female participants. Notably, an elevated CAR may be an independent risk factor for vulnerable carotid plaques (OR of upper: 2.06, 95% CI: 1.42-2.98, P < 0.001; P for trend <0.001).

Conclusion

A high CAR may be correlated with a high risk of carotid plaques, particularly among mildly aged adults (40-64 years) or females. Importantly, the CAR may be associated with vulnerable carotid plaques, suggesting that the CAR may be a new indicator for stroke prevention.

Early prediction of atherosclerosis diagnosis with medical ambient intelligence

Atherosclerosis is a chronic vascular disease that poses a significant threat to human health. Common diagnostic methods mainly rely on active screening, which often misses the opportunity for early detection. To overcome this problem, this paper presents a novel medical ambient intelligence system for the early detection of atherosclerosis by leveraging clinical data from medical records. The system architecture includes clinical data extraction, transformation, normalization, feature selection, medical ambient computation, and predictive generation. However, the heterogeneity of examination items from different patients can degrade prediction performance. To enhance prediction performance, the "SEcond-order Classifier (SEC)" is proposed to undertake the medical ambient computation task. The first-order component and second-order cross-feature component are then consolidated and applied to the chosen feature matrix to learn the associations between the physical examination data, respectively. The prediction is lastly produced by aggregating the representations. Extensive experimental results reveal that the proposed method's diagnostic prediction performance is superior to other state-of-the-art methods. Specifically, the Vitamin B12 indicator exhibits the strongest correlation with the early stage of atherosclerosis, while several known relevant biomarkers also demonstrate significant correlation in experimental data. The method proposed in this paper is a standalone tool, and its source code will be released in the future.

Fully automatic carotid arterial stiffness assessment from ultrasound videos based on machine learning

Arterial stiffness (AS) refers to the loss of arterial compliance and alterations in vessel wall properties. The study of local carotid stiffness (CS) is particularly important since carotid artery stiffening raises the risk of stroke, cognitive impairment, and dementia. So, stiffness measurement as a screening tool approach is crucial because it can reduce mortality and facilitate therapy planning. This study aims to evaluate the stiffness of the CCA using machine learning (ML) through the features of diameter change (ΔD) and stiffness parameters. This study was conducted in seven stages: data collection, preprocessing, CCA segmentation, CCA lumen diameter (DCCA) computing during cardiac cycles, denoising signals of DCCA, computational of AS parameters, and stiffness assessment using ML. The 51 videos (with 25 s) of CCA B-mode ultrasound (US) were used and analyzed. Each US video yielded approximately 750 sequential frames spanning about 24 cardiac cycles. Firstly, US preset settings with time gain compensation with a U-pattern were employed to enhance CCA segmentations. The study showed that auto region-growing, employed three times, is appropriate for segmenting walls with a short running time (4.56 s/frame). The diameter computed for frames constructs a signal (diameter signal) with noisy parts in the shape of peak variance and an un-smooth side. Among the 12 employed smoothing methods, spline fitting with a mean peak difference per cycle (MPDCY) of 0.58 pixels was the most effective for the diameter signal. The authors propose the MPDCY as a new selection criterion for smoothing methods with highly preserved peaks. The ΔD (D_sys-D_dia) determined in this study was validated by statistical analysis as a viable replacement for manual ΔD measurement. Statistical analysis was carried out by Mann-Whitney t-test with a p-value of 0.81, regression line R² = 0.907, and there was no difference in means between the two groups for box plots. The stiffness parameters of the carotid arteries were calculated based on auto-ΔD and pulse pressure. Five ML models, including K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and random forest (RF), fed by distension (ΔD) and five stiffness parameters, were used to distinguish between the stiffened and un-stiffened CCA. Except for SVM, all models performed excellently in terms of specificity, sensitivity, precision, and area under the curve (AUC). In addition, the scatterplot and statistical analysis of the fed features confirm these remarkable outcomes. The scatter plot demonstrates that a linear hyperline can easily distinguish between the two classes. The statistical analysis shows that the stiffness parameters computed from the database of this work were statistically (p < 0.05) distributed into the non-stiffness and stiffness groups. The presented models are validated by applying them to additional datasets. Applying models to other datasets reveals a model performance of 100%. The proposed ML models could be applied in clinical practice to detect CS early, which is essential for preventing stroke.

Associations between electrocardiogram and carotid ultrasound parameters: a healthy chinese group study

Background: Electrocardiogram (ECG) and carotid ultrasound (CUS) are important tools for the diagnosis and prediction of cardiovascular disease (CVD). This study aimed to investigate the associations between ECG and CUS parameters and explore the feasibility of assessing carotid health with ECG.

Methods: This cross-sectional cohort study enrolled 319 healthy Chinese subjects. Standard 12-lead ECG parameters (including the ST-segment amplitude [STA]), CUS parameters (intima-media thickness [IMT] and blood flow resistance index [RI]), and CVD risk factors (including sex, age, and systolic blood pressure [SBP]) were collected for analysis. Participants were divided into the high-level RI group (average RI ≥ 0.76, n = 171) and the normal RI group (average RI < 0.76, n = 148). Linear and stepwise multivariable regression models were performed to explore the associations between ECG and CUS parameters.

Results: Statistically significant differences in sex, age, SBP, STA and other ECG parameters were observed in the normal and the high-level RI group. The STA in lead V₃ yielded stronger significant correlations (r = 0.27–0.42, p < 0.001) with RI than STA in other leads, while ECG parameters yielded weak correlations with IMT (|r| ≤ 0.20, p < 0.05). STA in lead V₂ or V₃, sex, age, and SBP had independent contributions (p < 0.01) to predicting RI in the stepwise multivariable models, although the models for IMT had only CVD risk factors (age, body mass index, and triglyceride) as independent variables. The prediction model for RI in the left proximal common carotid artery (CCA) had higher adjusted R² (adjusted R² = 0.31) than the model for RI in the left middle CCA (adjusted R² = 0.29) and the model for RI in the right proximal CCA (adjusted R² = 0.20).

Conclusion: In a cohort of healthy Chinese individuals, the STA was associated with the RI of CCA, which indicated that ECG could be utilized to assess carotid health. The utilization of ECG might contribute to a rapid screening of carotid health with convenient operations.

Soft Attention Based DenseNet Model for Parkinson’s Disease Classification Using SPECT Images

Objective

Deep learning algorithms have long been involved in the diagnosis of severe neurological disorders that interfere with patients’ everyday tasks, such as Parkinson’s disease (PD). The most effective imaging modality for detecting the condition is DaTscan, a variety of single-photon emission computerized tomography (SPECT) imaging method. The goal is to create a convolutional neural network that can specifically identify the region of interest following feature extraction.

Methods

The study comprised a total of 1,390 DaTscan imaging groups with PD and normal classes. The architecture of DenseNet-121 is leveraged with a soft-attention block added before the final classification layer. For visually analyzing the region of interest (ROI) from the images after classification, Soft Attention Maps and feature map representation are used.

Outcomes

The model obtains an overall accuracy of 99.2% and AUC-ROC score 99%. A sensitivity of 99.2%, specificity of 99.4% and f1-score of 99.1% is achieved that surpasses all prior research findings. Soft-attention map and feature map representation aid in highlighting the ROI, with a specific attention on the putamen and caudate regions.

Conclusion

With the deep learning framework adopted, DaTscan images reveal the putamen and caudate areas of the brain, which aid in the distinguishing of normal and PD cohorts with high accuracy and sensitivity.

Spatiotemporal reconstruction method of carotid artery ultrasound from freehand sonography

PURPOSE

4D reconstruction based on radiation-free ultrasound can provide valuable information about the anatomy. Current 4D US technologies are either faced with limited field-of-view (FoV), technical complications, or cumbersome setups. This paper proposes a spatiotemporal US reconstruction framework to enhance its ability to provide dynamic structure information.

METHODS

We propose a spatiotemporal US reconstruction framework based on freehand sonography. First, a collecting strategy is presented to acquire 2D US images in multiple spatial and temporal positions. A morphology-based phase extraction method after pose correction is presented to decouple the compounding image variations. For temporal alignment and reconstruction, a robust kernel regression model is established to reconstruct images in arbitrary phases. Finally, the spatiotemporal reconstruction is demonstrated in the form of 4D movies by integrating the US images according to the tracked poses and estimated phases.

RESULTS

Quantitative and qualitative experiments were conducted on the carotid US to validate the feasibility of the proposed pipeline. The mean phase localization and heart rate estimation errors were 0.07 ± 0.04 s and 0.83 ± 3.35 bpm, respectively, compared with cardiac gating signals. The assessment of reconstruction quality showed a low RMSE (<0.06) between consecutive images. Quantitative comparisons of anatomy reconstruction from the generated US volumes and MRI showed an average surface distance of 0.39 ± 0.09 mm on the common carotid artery and 0.53 ± 0.05 mm with a landmark localization error of 0.60 ± 0.18 mm on carotid bifurcation.

CONCLUSION

A novel spatiotemporal US reconstruction framework based on freehand sonography is proposed that preserves the utility nature of conventional freehand US. Evaluations on in vivo datasets indicated that our framework could achieve acceptable reconstruction performance and show potential application value in the US examination of dynamic anatomy.

Emerging Feature Extraction Techniques for Machine Learning-Based Classification of Carotid Artery Ultrasound Images

Plaque deposits in the carotid artery are the major cause of stroke and atherosclerosis. Ultrasound imaging is used as an early indicator of disease progression. Classification of the images to identify plaque presence and intima-media thickness (IMT) by machine learning algorithms requires features extracted from the images. A total of 361 images were used for feature extraction, which will assist in further classification of the carotid artery. This study presents the extraction of 65 features, which constitute of shape, texture, histogram, correlogram, and morphology features. Principal component analysis (PCA)-based feature selection is performed, and the 22 most significant features, which will improve the classification accuracy, are selected. Naive Bayes algorithm and dynamic learning vector quantization (DLVQ)-based machine learning classifications are performed with the extracted and selected features, and analysis is performed.

A Low-Cost Multistage Cascaded Adaptive Filter Configuration for Noise Reduction in Phonocardiogram Signal

Phonocardiogram (PCG), the graphic recording of heart signals, is analyzed to determine the cardiac mechanical function. In the recording of PCG signals, the major problem encountered is the corruption by surrounding noise signals. The noise-corrupted signal cannot be analyzed and used for advanced processing. Therefore, there is a need to denoise these signals before being employed for further processing. Adaptive Noise Cancellers are best suited for signal denoising applications and can efficiently recover the corrupted PCG signal. This paper introduces an optimal adaptive filter structure using a Sign Error LMS algorithm to estimate a noise-free signal with high accuracy. In the proposed filter structure, a noisy signal is passed through a multistage cascaded adaptive filter structure. The number of stages to be cascaded and the step size for each stage are adjusted automatically. The proposed Variable Stage Cascaded Sign Error LMS (SELMS) adaptive filter model is tested for denoising the fetal PCG signal taken from the SUFHS database and corrupted by Gaussian and colored pink noise signals of different input SNR levels. The proposed filter model is also tested for pathological PCG signals in the presence of Gaussian noise. The simulation results prove that the proposed filter model performs remarkably well and provides 8–10 dB higher SNR values in a Gaussian noise environment and 2-3 dB higher SNR values in the presence of colored noise than the existing cascaded LMS filter models. The MSE values are improved by 75–80% in the case of Gaussian noise. Further, the correlation between the clean signal and its estimate after denoising is more than 0.99. The PSNR values are improved by 7 dB in a Gaussian noise environment and 1-2 dB in the presence of pink noise. The advantage of using the SELMS adaptive filter in the proposed filter model is that it offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy.

Diffusion-Weighted Imaging Combined with Cervical Vascular Ultrasound in the Elderly Patients with Multiple Cerebral Infarction

Objective

This study is aimed at evaluating the diagnostic value of diffusion-weighted imaging combined with cervical vascular ultrasound in the elderly patients with multiple cerebral infarctions and at demonstrating whether the diagnostic value is affected by the history of diabetes.

Methods

From January 2020 to November 2021, the case data of 30 elderly patients with multiple cerebral infarction diagnosed in our hospital were included. Diffusion-weighted magnetic resonance imaging (DWI) and cervical vascular ultrasound (CAU) were performed, respectively. The diagnosis rates of the simple diffusion-weighted imaging group, the simple cervical vascular ultrasound group, and the diffusion-weighted imaging combined with cervical vascular ultrasound group were compared.

Results

The median onset time of 30 patients was 26.5 (4.75, 43.0) hours. There were 10 hyperacute patients and 20 acute patients. The proportion of diagnosable patients in the diffusion-weighted imaging group was 83.8% (25/30), which was lower than the proportion of diagnosable patients in the diffusion-weighted imaging combined with cervical vascular ultrasound group, which was 90% (27/30). The difference was statistically significant (χ² = 16.667; P < 0.001). The ratio of diagnosable patients in the cervical vascular ultrasound group alone was 66.7% (20/30), which was lower than 90% (27/30) in the diffusion-weighted imaging combined with cervical vascular ultrasound group. The difference was statistically significant (χ² = 6.7; P = 0.010). In the hyperacute phase, the proportion of diagnosable patients in the diffusion-weighted imaging combined with cervical vascular ultrasound group was higher than that in the diffusion-weighted imaging group alone (χ² = 5.833; P = 0.016) and the cervical vascular ultrasound group alone (χ² = 2.500; P = 0.004). In the acute phase, the proportion of diagnosable patients in the diffusion-weighted imaging combined with cervical vascular ultrasound group was also higher those that in the diffusion-weighted imaging group alone (χ² = 9.474; P = 0.002) and the cervical vascular ultrasound group alone (χ² = 3.158; P = 0.006). The diagnostic accuracy of diffusion-weighted imaging combined with cervical vascular ultrasound was not significantly different between patients with history of diabetes and without history of diabetes (χ² = 1.014; P = 0.314)

Conclusion

The combined application of diffusion-weighted imaging and cervical vascular ultrasound has important value in improving the diagnosis rate of multiple cerebral infarction in the elderly, regardless of diabetes history, and it is worthy of clinical application.

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.

Pictorial Essay of Cervical Duplex Ultrasonography

Objectives: Cervical duplex ultrasonography (CDU) is a simple, non-invasive, portable technique, that provides valuable high-quality visual information about the integrity of the carotid and vertebral vessels, plaque morphology and flow hemodynamics. CDU is useful in the assessment and follow up of patients with cerebrovascular disease as well as other conditions like inflammatory vasculitis, carotid artery dissection and carotid body tumours. CDU is inexpensive and invaluable in smaller centres. Methods: CDU was performed in all patients in both longitudinal and transverse planes in the out-patient clinic. Brightness mode (B-mode) and Doppler waveforms were obtained. Relevant findings were presented. Results: CDU provides real time visualisation of plaque characteristics and follow up, hemodynamic characteristics in Takayasu arteritis, visualisation of dissection. Conclusion: With availability of MR/CT angiography, CDU can be an adjuvant in follow up, triage and early bed-side diagnosis of the vascular diseases. We present our experience with CDU in the out-patient clinics in this pictorial essay.

Contrast enhanced sonothrombolysis using streptokinase loaded phase change nano-droplets for potential treatment of deep venous thrombosis

Current thrombolytic therapies for deep venous thrombosis are limited due to the wide side effect profile. Contrast mediated sonothrombolysis is a promising approach for thrombus treatment. The current study examines the effectiveness of in vitro streptokinase (SK) loaded phase-change nanodroplet (PCND) mediated sonothrombolysis at 7 MHz for the diagnosis of deep venous thrombosis. Lecithin shell and perfluorohexane core nanodroplets were prepared via the thin-film hydration method and morphologically characterized. Sonothrombolysis was performed at 7 MHz at different mechanical indexes of samples i.e., only sonothrombolysis, PCND mediated sonothrombolysis, sonothrombolysis with SK and SK loaded PCND mediated sonothrombolysis. Thrombolysis efficacy was assessed by measuring clot weight changes during 30 min US exposure, recording the mean gray intensity from the US images of the clot by computer software ImageJ, and spectrophotometric quantification of the hemoglobin in the clot lysate. In 15 minutes of sonothrombolysis performed at high mechanical index (0.9 and 1.2), SK loaded PCNDs showed a 48.61% and 74.29% reduction of mean gray intensity. At 0.9 and 1.2 MI, 86% and 92% weight loss was noted for SK-loaded PCNDs in confidence with spectrophotometric results. A significant difference (P < 0.05) was noted for SK-loaded PCND mediated sonothrombolysis compared to other groups. Loading of SK inside the PCNDs enhanced the efficacy of sonothrombolysis. An increase in MI and time also increased the efficacy of sonothrombolysis. This in vitro study showed the potential use of SK-loaded perfluorohexane core PCNDs as sonothrombolytic agents for deep venous thrombosis.

Contrast enhanced sonothrombolysis using streptokinase loaded phase change nano-droplets.

Optimized integrated design of a high-frequency medical ultrasound transducer with genetic algorithm

Designing efficient acoustic stack and elements for high-frequency (HF) medical ultrasound (US) transducers involves various interrelated parameters. So far, optimizing spatial resolution and acoustic field intensity simultaneously has been a daunting task in the area of HF medical US imaging. Here, we introduce optimized design for a 50-MHz US probe for skin tissue imaging. We have developed an efficient design and simulation approach using Krimholtz, Leedom and Matthaei (KLM) equivalent circuit model and spatial impulse response method by means of Field II software. These KLM model and Field II software are integrated, and a GA algorithm is used to optimize the design of the US transducer to obtain the best imaging performance. As a result, a 50-MHz single element probe is effectively optimized with 5 mm acoustic focal length, 72 $$\upmu {\text{m}}$$ μ m lateral, and 42 $$\upmu {\text{m}}$$ μ m axial imaging resolution, with an enhancement in imaging resolution over the conventionally designed and simulated probe by 10%. This work has the potential to benefit many applications that require a fast, high-resolution and strong US focus in skin imaging.

Artificial Intelligence Segmented Dynamic Video Images for Continuity Analysis in the Detection of Severe Cardiovascular Disease

In this paper, an artificial intelligence segmented dynamic video image based on the process of intensive cardiovascular and cerebrovascular disease monitoring is deeply investigated, and a sparse automatic coding deep neural network with a four layers stack structure is designed to automatically extract the deep features of the segmented dynamic video image shot, and six categories of normal, atrial premature, ventricular premature, right bundle branch block, left bundle branch block, and pacing are achieved through hierarchical training and optimization. Accurate recognition of heartbeats with an average accuracy of 99.5%. It provides technical assistance for the intelligent prediction of high-risk cardiovascular diseases like ventricular fibrillation. An intelligent prediction algorithm for sudden cardiac death based on the echolocation network was proposed. By designing an echolocation network with a multilayer serial structure, an intelligent distinction between sudden cardiac death signal and non-sudden death signal was realized, and the signal was predicted 5 min before sudden death occurred, with an average prediction accuracy of 94.32%. Using the self-learning capability of stack sparse auto-coding network, a large amount of label-free data is designed to train the stack sparse auto-coding deep neural network to automatically extract deep representations of plaque features. A small amount of labeled data then introduced to micro-train the entire network. Through the automatic analysis of the fiber cap thickness in the plaques, the automatic identification of thin fiber cap-like vulnerable plaques was achieved, and the average overlap of vulnerable regions reached 87%. The overall time for the automatic plaque and vulnerable plaque recognition algorithm was 0.54 s. It provides theoretical support for accurate diagnosis and endogenous analysis of high-risk cardiovascular diseases.

Fully Automated Integrated Segmentation of Carotid Artery Ultrasound Images Using DBSCAN and Affinity Propagation

Purpose

B-mode ultrasound images are used in identifying the presence of fat deposit if any in carotid artery. The intima media, lumen, bifurcation boundary is detected by the echogenic characteristics embedded in the carotid artery.

Methods

A fully automatic self-learning based segmentation is proposed by extracting the edges by a modified affinity propagation, which are given as inputs to the Density Based Spatial Clustering of Applications with Noise (DBSCAN) for super pixel segmentation. The segmented results are analyzed with Gradient Vector Flow (GVF) snake model and Particle Swarm Optimization (PSO) clustering based segmentation using various performance measures.

Results

The proposed parameter free, fully automatic segmentation method combining Affinity propagation and DBSCAN are evaluated for a database of 361 images and gives reinforced results in the longitudinal B-mode ultrasound images. The proposed approach gives an improved accuracy of 12% increase when compared with the manual segmentation and 15% compared with segmentation by affinity propagation and DBSCAN when performed individually. The average Root Mean Square Error (RMSE) is 110 ± 44 µm.

Conclusion

Extracted edge points are used for clustering in a fully automated carotid artery segmentation approach.

Towards to Optimal Wavelet Denoising Scheme-A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

Wavelet transformation is one of the most frequent procedures for data denoising, smoothing, decomposition, features extraction, and further related tasks. In order to perform such tasks, we need to select appropriate wavelet settings, including particular wavelet, decomposition level and other parameters, which form the wavelet transformation outputs. Selection of such parameters is a challenging area due to absence of versatile recommendation tools for suitable wavelet settings. In this paper, we propose a versatile recommendation system for prediction of suitable wavelet selection for data smoothing. The proposed system is aimed to generate spatial response matrix for selected wavelets and the decomposition levels. Such response enables the mapping of selected evaluation parameters, determining the efficacy of wavelet settings. The proposed system also enables tracking the dynamical noise influence in the context of Wavelet efficacy by using volumetric response. We provide testing on computed tomography (CT) and magnetic resonance (MR) image data and EMG signals mostly of musculoskeletal system to objectivise system usability for clinical data processing. The experimental testing is done by using evaluation parameters such is MSE (Mean Squared Error), ED (Euclidean distance) and Corr (Correlation index). We also provide the statistical analysis of the results based on Mann-Whitney test, which points out on statistically significant differences for individual Wavelets for the data corrupted with Salt and Pepper and Gaussian noise.