Objective.The objective of this study is to evaluate the effectiveness of monotherapeutic high-intensity focused ultrasound (HIFU) for thrombolysis in highly occluded vessels using anin vitromodel, with emphasis on thermal safety.Approach.HIFU was applied to vessel phantom with various occlusion ratios (60%, 70%, 80%, 90%, and 100%) of the lumen cross-sectional area, each with an equal clot length of 15 mm, under a pulsatile flow of 5 ml min-1generated by a peristaltic pump operated at 70 rpm. The insonation was conducted at a frequency of 1.1 MHz, power (Pelect) of 180 W, duty cycle of 0.4%, pulse repetition frequency of 1 Hz and total exposure time of 30 min. A step-by-step exposure technique was employed, where the focus of the HIFU beam was progressively moved from one end of the clot to the other. A multi-layer simulation model was used to estimate the acoustic pressure and temperature at the focal point, with temperature distribution monitored throughout the exposure to ensure thermal safety. Thrombolysis efficiency was assessed by measuring the pre- and post-treatment clot weights.Main results.HIFU achieved a thrombolysis efficiency (defined as fractional reduction in clot weight) of 65.99% at 100% occlusion when high power and step-by-step exposure were used. This efficiency increased to 78.6% at 60% occlusion. These results were achieved while ensuring thermal safety by maintaining the temperature below 43 °C.Significance.This study further confirms that HIFU is a promising non-invasive method for thrombolysis in highly occluded vessels while ensuring thermal safety by using high power and a step-by-step exposure method.

Objective:This study was designed to investigate the dose-response relationship between the triglyceride-glucose (TyG) index and the occurrence of in-hospital major adverse cardiovascular events (MACE) in patients with acute myocardial infarction (AMI), and to examine whether a nonlinear association pattern exists. Methods:This single-center retrospective cohort study consecutively enrolled 1,065 patients diagnosed with type 1 AMI who underwent coronary angiography at the Department of Cardiology, The Second Affiliated Hospital of Shenyang Medical College, between June 2022 and June 2025. Baseline data were collected from the electronic medical record system, and the TyG index was calculated using the formula: TyG = Ln [triglycerides (mg/dL) × fasting plasma glucose (mg/dL)]/2. The primary outcome was in-hospital MACE (occurring during hospitalization, ≤7 days after admission), which was a composite of all-cause mortality, acute heart failure, malignant arrhythmia, recurrent myocardial infarction, and cardiogenic shock. Patients were categorized into three groups [T1 [low], T2 [medium], and T3 [high]] based on the overall tertiles of the TyG index. Multivariable logistic regression analysis was employed to construct hierarchical models (a base model, a parsimonious model, and a core model) and to analyze the association between TyG index groups and MACE. Model performance was evaluated by assessing discrimination (area under the curve, AUC), calibration, and clinical utility. Restricted cubic splines (RCS) were used to explore the potential nonlinear relationship between the TyG index (as a continuous variable) and MACE. The inflection point was identified, and piecewise regression analysis was performed. Subgroup analyses were conducted to assess the heterogeneity of the association. Results:This study included 1,065 patients with AMI, who were stratified into T1 (low, n = 352), T2 (medium, n = 361), and T3 (high, n = 352) groups based on TyG index tertiles. Baseline analysis revealed significant differences among the three groups in body mass index (BMI), left ventricular ejection fraction (LVEF), age, diastolic blood pressure (DBP), heart rate (HR), fasting plasma glucose (FPG), serum creatinine (Scr), lipid profiles (total cholesterol [TC], triglycerides [TG], high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C]), peak cardiac troponin T (cTnT), peak N-terminal pro-B-type natriuretic peptide (NT-proBNP), prevalence of diabetes, and usage rates of angiotensin system inhibitors (ACEI/ARB/ARNI) and beta-blockers (all p < 0.05). The overall incidence of in-hospital MACE was 21.13%. Notably, the T2 group exhibited a significantly higher incidence (26.04%) compared to the T1 group (16.76%, p = 0.011). In the core multivariable logistic regression model (adjusted for age, sex, BMI, LVEF, diabetes, cTnT, NT-proBNP, etc.), the T2 group was associated with a 178% significantly increased risk of in-hospital MACE compared to the T1 group [odds ratio [OR] = 2.78, 95% confidence interval [CI]: 1.38-5.59, p = 0.004]. The core model incorporating the TyG index groups demonstrated the best predictive performance [area under the receiver operating characteristic curve (AUC) = 0.79, 95% CI: 0.75-0.83], good calibration (Hosmer-Lemeshow test p = 0.883), and was confirmed to provide clinical net benefit by decision curve analysis. Restricted cubic spline analysis revealed a significant J-shaped relationship between the TyG index and the risk of in-hospital MACE, with an inflection point identified at TyG = 9.05. Piecewise regression analysis demonstrated that when the TyG index was <9.05, the MACE risk did not change significantly (OR = 1.04, 95% CI: 0.62-1.77, P = 0.875); however, for every 1-unit increase in the TyG index ≥ 9.05, the MACE risk significantly increased by 124% (OR = 2.24, 95% CI: 1.26-3.96, P = 0.006). Subgroup analyses (stratified by sex, smoking status, hypertension, diabetes, age, and BMI) revealed no significant heterogeneity in the association between the TyG index (≥inflection point vs. 0.05), supporting the consistency of this J-shaped association across the studied population subgroups. Conclusion:This study demonstrates a significant J-shaped nonlinear association between the TyG index and the risk of in-hospital MACE, with an identified inflection point at 9.05. Notably, patients with a medium TyG level (T2 group) exhibited the highest MACE risk, which was significantly greater than that of patients with a low TyG level (T1 group). This finding challenges the conventional linear perception that a higher TyG index consistently correlates with greater risk. The core model incorporating TyG index stratification significantly improved the predictive performance for MACE. These findings collectively underscore the importance of considering the nonlinear effect of the TyG index in risk assessment for AMI patients, suggesting that individuals with medium TyG levels may represent a specific high-risk subgroup warranting particular attention.

OBJECTIVE:Stand-alone high-intensity focused ultrasound (HIFU) therapy is a promising non-invasive approach for treating thrombo-occlusive disease. The objective of this study was to investigate the ability of this approach to achieve safe thrombolysis in clinical treatment. METHODS:Two types of thrombo-occlusive models, utilizing plastic tubes (model-Ⅰ) and the abdominal aorta of rabbits (model-Ⅱ), were exposed to 1.1-MHz HIFU with a pulse repetition frequency (PRF) of 1-100 Hz and transducer power (Pelect) up to 180 W. A duty cycle of 0.6% was set to maintain tissue temperature below 43℃. RESULTS:The experimental results from model-I demonstrated that extensive thrombolysis was seen at Pelect ≥ 120 W and PRF ≤ 10 Hz. In the experiments using model-Ⅱ (Pelect = 120-180 W, PRF = 1 Hz) a degree of thrombolysis of over 70% was found with treatment times between 12 and 33 minutes. Under these conditions the arteries appeared to have suffered only minor damage, with slight laceration of the intima/inner media and separation of medial lamellae. The mean damage score did not exceed the threshold for vascular rupture at any power level, with no significant differences observed (n = 3, p > 0.05), but slightly lower values were noted at lower power levels. The maximum diameter of the clot debris was < 8 μm, therefore comparable to that of a typical capillary, minimizing the danger of distal embolization. CONCLUSION:The results confirm the potential of stand-alone HIFU as an effective treatment of thrombo-occlusive disease without causing significant vascular damage.

Objective: Coronary artery segmentation is a prerequisite for assessing the severity of coronary artery disease (CAD), which has a high prevalence and mortality rate. However, segmentation of the coronary arteries is challenging due to their complex anatomy diverse morphology, and small target size. Methods: This study proposes a dual-factor iterative enhancement network (DIEN) for automatic coronary artery segmentation in coronary computed tomography angiographic (CCTA) images. The DIEN includes a dual-factor layer-by-layer iterative convolution (DLIC) module and a global recalibration feature selection (GRFS) module. The DLIC in the encoding part aims to capture diverse semantic information from coronary arteries with different morphologies based on extracting multiple features under different receptive fields. The GRFS bridges the semantic gap between the encoding and decoding parts by exploiting the global information of the slices to improve the feature representation of the coronary artery. Results: Experiments show that the DIEN outperforms seven state-of-the-art algorithms on a variety of metrics on two datasets. On a self-collected dataset, the DIEN achieved a 1.01 %, 0.69 %, 1.91 %, and 0.63 % improvement over U-Net in IoU, dice, recall, and precision, respectively. In comparison to U-Net, the IoU, dice, recall, and precision obtained by the DIEN on the public dataset were improved by 1.03 %, 0.73 %, 0.12 %, and 0.23 %, respectively. Conclusion: Compared with state-of-the-art approaches, the proposed model achieves good performance in segmenting coronary arteries both on our dataset and a publicly available dataset. Significance: Our method can accurately segment coronary arteries, and can contribute to improved diagnosis and assessment of CAD.

Background:Ferulic acid (FA) is an antioxidant compound present in cereals, fruits, and vegetables. Chronic consumption of a high-fat and high-carbohydrate (HFHC) diet can lead to metabolic syndrome and increase the risk of atherosclerotic cardiovascular disease. This study examined whether FA could mitigate vascular inflammation, aortic stiffness, and cardiovascular remodeling in rats fed a HFHC diet. Methods:Male Sprague-Dawley rats were divided into five groups (eight rats/group): one group was fed a standard chow diet with or without FA supplementation, while the others were fed a HFHC diet plus a 15% fructose solution for 16 weeks. Rats on the HFHC diet received FA at doses of 0, 30, or 60 mg/kg/day during the final 6 weeks of the study. Various cardiovascular parameters, plasma biochemical markers, and the expression of biomarker proteins were measured. Results:FA administration alleviated the metabolic disturbances caused by the HFHC diet. FA reduced arterial blood pressure, aortic pulse wave velocity, oxidative stress, vascular inflammation, and angiotensin-mediated myocardial fibrosis and cardiac hypertrophy, as evidenced by decreases in ventricular interstitial fibrosis and cross-sectional area. These beneficial effects were associated with reduced vascular superoxide production and lower plasma levels of angiotensin-converting enzyme and tumor necrosis factor α. FA also suppressed the expression of Ang II type 1 receptor, gp91phox, and vascular-adhesion molecule 1 proteins and prevented hypertrophic remodeling of the aortic wall by reducing protein expression of matrix metalloproteinases 2 and 9. Conclusion:This study provides insightful findings on the beneficial effects of FA in reducing aortic stiffness and cardiovascular remodeling associated with metabolic syndrome.

Background and Objective: Epicardial Adipose Tissue (EAT) is regarded as an independent risk factor for cardiovascular disease, and an increase in its volume is closely associated with disorders such as coronary artery atherosclerosis. Traditional manual and semi-automatic methods for EAT segmentation rely on subjective judgment, resulting in uncertainty and unreliability, which limits their application in clinical practice. Therefore, this study aims to develop a fully automatic segmentation and quantification method to improve the accuracy of EAT assessment. Methods: A Boundary-Enhanced Multi-scale U-Net network with a Convolutional Transformer (BMT-UNet) is developed to segment the pericardium. The BMT-UNet comprises Boundary-Enhanced (BE) modules, Multi-Scale (MS) modules, and a Convolutional Transformer (ConvT) module. The MS and BE modules in the encoding part are designed to capture detailed boundary features and accurately delineate the pericardium boundary by combining multi-scale features with morphological operations, leveraging their complementarity. The ConvT module integrates global contextual information, thereby enhancing overall segmentation accuracy and addressing the issue of internal holes in the segmented pericardial images. The volume of EAT is automatically quantified using standard fat thresholds with a range of -190 to -30 HU. Results: For a Coronary Computed Tomography Angiography (CCTA) dataset which contained 50 patients, the Dice coefficient and Hausdorff distance for the proposed method of pericardial and EAT segmentation are 98.3% + 0.2%, 5.7+0.8 mm, and 93.9% + 1.7%, 2.1 + 0.3 mm, respectively. The linear regression coefficient between the EAT volume segmented and the actual volume is 0.982, and the Pearson correlation coefficient is 0.99. BlandAltman analysis further confirmed the high consistency between the automated and manual methods. These results demonstrate a significant improvement over existing methods, particularly in terms of segmentation precision and reliability, which are critical for clinical application. Conclusions: This work develops an automated method for quantifying EAT in Computed Tomography (CT) images, and the results agreed closely with expert evaluations. Code is available at: https://github.com/wy-9903 /BMT-UNet.

In bone cancer imaging, positron emission tomography (PET) is ideal for the diagnosis and staging of bone cancers due to its high sensitivity to malignant tumors. The diagnosis of bone cancer requires tumor analysis and localization, where accurate and automated wholebody bone segmentation (WBBS) is often needed. Current WBBS for PET imaging is based on paired Computed Tomography (CT) images. However, mismatches between CT and PET images often occur due to patient motion, which leads to erroneous bone segmentation and thus, to inaccurate tumor analysis. Furthermore, there are some instances where CT images are unavailable for WBBS. In this work, we propose a novel multimodal fusion network (MMF-Net) for WBBS of PET images, without the need for CT images. Specifically, the tracer activity (lambda-MLAA), attenuation map (mu-MLAA), and synthetic attenuation map (mu-DL) images are introduced into the training data. We first design a multi-encoder structure employed to fully learn modalityspecific encoding representations of the three PET modality images through independent encoding branches. Then, we propose a multimodal fusion module in the decoder to further integrate the complementary information across the three modalities. Additionally, we introduce revised convolution units, SE (Squeeze-and-Excitation) Normalization and deep supervision to improve segmentation performance. Extensive comparisons and ablation experiments, using 130 whole-body PET image datasets, show promising results. We conclude that the proposed method can achieve WBBS with moderate to high accuracy using PET information only, which potentially can be used to overcome the current limitations of CT-based approaches, while minimizing exposure to ionizing radiation.

This review paper explores post-prandial glycemia in type 2 diabetes. Post-prandial glycemia is defined as the period of blood glucose excursion from immediately after the ingestion of food or drink to 4 to 6 hours after the end of the meal. Post-prandial hyperglycemia is an independent risk factor for cardiovascular disease with glucose "excursions" being more strongly associated with markers of oxidative stress than the fasting or pre-prandial glucose level. High blood glucose is a major promoter of enhanced free radical production and is associated with the onset and progression of type 2 diabetes. Oxidative stress impairs insulin action creating a vicious cycle where repeated post-prandial glucose spikes are key drivers in the pathogenesis of the vascular complications of type 2 diabetes, both microvascular and macrovascular. Some authors suggest post-prandial hyperglycemia is the major cause of death in type 2 diabetes. Proper management of post-prandial hyperglycemia could yield up to a 35% cut in overall cardiovascular events, and a 64% cut in myocardial infarction. The benefits of managing post-prandial hyperglycemia are similar in magnitude to those seen in type 2 diabetes patients receiving secondary prevention with statins - prevention which today is regarded as fundamental by all practitioners. Given all the evidence surrounding the impact of post-prandial glycemia on overall outcome, it is imperative that any considered strategy for the management of type 2 diabetes should include optimum dietary, pharma, and lifestyle interventions that address glucose excursion. Achieving a low post-prandial glucose response is key to prevention and progression of type 2 diabetes and cardiometabolic diseases. Further, such therapeutic interventions should be sustainable and must benefit patients in the short and long term with the minimum of intrusion and side effects. This paper reviews the current literature around dietary manipulation of post-prandial hyperglycemia, including novel approaches. A great deal of further work is required to optimize and standardize the dietary management of post-prandial glycemia in type 2 diabetes, including consideration of novel approaches that show great promise.

BACKGROUND:Carotid-femoral pulse wave velocity (cfPWV) is the gold standard for noninvasive arterial stiffness assessment, an independent predictor of cardiovascular disease, and a potential parameter to guide therapy. However, cfPWV is not routinely measured in clinical practice due to the unavailability of a low-cost, operator-friendly, and independent device. The current study validated a novel laser Doppler vibrometry (LDV)-based measurement of cfPWV against the reference technique. METHODS:In 100 (50 men) hypertensive patients, cfPWV was measured using applanation tonometry (Sphygmocor) and the novel LDV device. This device has 2 handpieces with 6 laser beams each that simultaneously measure vibrations from the skin surface at carotid and femoral sites. Pulse wave velocity is calculated using ECG for the identification of cardiac cycles. An ECG-independent method was also devised. Cardiovascular risk score was calculated for patients between 40 and 75 years old using the WHO risk scoring chart. RESULTS:LDV-based cfPWV correlated significantly with tonometry (r=0.86, P<0.0001 ECG-dependent [cfPWVLDV_ECG] and r=0.80, P<0.001 ECG-independent [cfPWVLDV_w/oECG] methods). Bland-Altman analysis showed nonsignificant bias (0.65 m/s) and acceptable SD (1.27 m/s) between methods. Intraobserver coefficient of variance for LDV was 4.7% (95% CI, 3.0%-5.5%), and interobserver coefficient of variance was 5.87%. CfPWV correlated significantly with CVD risk (r=0.64, P<0.001; r=0.41, P=0.003; and r=0.37, P=0.006 for tonometry, LDV-with, and LDV-without ECG, respectively). CONCLUSIONS:The study demonstrates clinical validity of the LDV device. The LDV provides a simple, noninvasive, operator-independent method to measure cfPWV for assessing arterial stiffness, comparable to the standard existing techniques. REGISTRATION:URL: https://clinicaltrials.gov/study/NCT03446430; Unique identifier: NCT03446430.

Coronary Artery Disease (CAD) results from plaque deposit in a coronary artery. Early diagnosis is imperative, so a non-invasive detection method is being developed to identify acoustic signals caused by partial occlusions in the artery. The blood flow in the artery is disturbed and imposes oscillatory stresses on the artery wall. The deformations caused by the stresses can be detected at the chest surface. Therefore, by using data simulating these surface signals, which arise from randomly assigned source positions, machine learning (ML) can be utilised to predict the source of the occlusion. Seven ML algorithms were investigated, and the results from this study found that an ensemble model combining k-Nearest Neighbours and Random Forest had the best performance. The metrics used to evaluate this was the mean squared error and Euclidean distance.

BackgroundThe curved planar reformation (CPR) technique is one of the most commonly used methods in clinical practice to locate coronary arteries in medical images.PurposeThe artery centerline is the cornerstone for the generation of the CPR image. Here, we describe the development of a new fully automatic artery centerline tracker with the aim of increasing the efficiency and accuracy of the process.MethodsWe propose a COronary artery Centerline Tracker (COACT) framework which consists of an ostium point finder (OPFinder) model, an intersection point detector (IPDetector) model and a set of centerline tracking strategies. The output of OPFinder is the ostium points. The function of the IPDetector is to predict the intersections of a sample sphere and the centerlines. The centerline tracking process starts from two ostium points detected by the OPFinder, and combines the results of the IPDetector with a series of strategies to gradually reconstruct the coronary artery centerline tree.ResultsTwo coronary CT angiography (CCTA) datasets were used to validate the models. Dataset1 contains 160 cases (32 for test and 128 for training) and dataset2 contains 70 cases (20 for test and 50 for training). The results show that the average distance between the ostium points predicted by the OPFinder and the manually annotated ostium points was 0.88 mm, which is similar to the differences between the results obtained by two observers (0.85 mm). For the IPDetector, the average overlap of the predicted and ground truth intersection points was 97.82% and this is also close to the inter-observer agreement of 98.50%. For the entire coronary centerline tree, the overlap between the results obtained by COACT and the gold standard was 94.33%, which is slightly lower than the inter-observer agreement, 98.39%.ConclusionsWe have developed a fully automatic centerline tracking method for CCTA scans and achieved a satisfactory result. The proposed algorithms are also incorporated in the medical image analysis platform TIMESlice () for further studies.

BACKGROUND AND OBJECTIVE:Aortic blood pressure (ABP) is a more effective prognostic indicator of cardiovascular disease than peripheral blood pressure. A highly accurate algorithm for non-invasively deriving the ABP wave, based on ultrasonic measurement of aortic flow combined with peripheral pulse wave measurements, has been proposed elsewhere. However, it has remained at the proof-of-concept stage because it requires a priori knowledge of the ABP waveform to calculate aortic pulse wave velocity (PWV). The objective of this study is to transform this proof-of-concept algorithm into a clinically feasible technique.METHODS:We used the Bramwell-Hill equation to non-invasively calculate aortic PWV which was then used to reconstruct the ABP waveform from non-invasively determined aortic blood flow velocity, aortic diameter, and radial pressure. The two aortic variables were acquired by an ultrasound system from 90 subjects, followed by recordings of radial pressure using a SphygmoCor device. The ABPs estimated by the new algorithm were compared with reference values obtained by cardiac catheterization (invasive validation, 8 subjects aged 62.3 ± 12.7 years) and a SphygmoCor device (non-invasive validation, 82 subjects aged 45.0 ± 17.8 years).RESULTS:In the invasive comparison, there was good agreement between the estimated and directly measured pressures: the mean error in systolic blood pressure (SBP) was 1.4 ± 0.8 mmHg; diastolic blood pressure (DBP), 0.9 ± 0.8 mmHg; mean blood pressure (MBP), 1.8 ± 1.2 mmHg and pulse pressure (PP), 1.4 ± 1.1 mmHg. In the non-invasive comparison, the estimated and directly measured pressures also agreed well: the errors being: SBP, 2.0 ± 1.4 mmHg; DBP, 0.8 ± 0.1 mmHg; MBP, 0.1 ± 0.1 mmHg and PP, 2.3 ± 1.6 mmHg. The significance of the differences in mean errors between calculated and reference values for SBP, DBP, MBP and PP were assessed by paired t-tests. The agreement between the reference methods and those obtained by applying the new approach was also expressed by correlation and Bland-Altman plots.CONCLUSION:The new method proposed here can accurately estimate ABP, allowing this important variable to be obtained non-invasively, using standard, well validated measurement techniques. It thus has the potential to relocate ABP estimation from a research environment to more routine use in the cardiac clinic.SHORT ABSTRACT:A highly accurate algorithm for non-invasively deriving the ABP wave has been proposed elsewhere. However, it has remained at the proof-of-concept stage because it requires a priori knowledge of the ABP waveform to calculate aortic pulse wave velocity (PWV). This study aims to transform this proof-of-concept algorithm into a clinically feasible technique. We used the Bramwell-Hill equation to non-invasively calculate aortic PWV which was then used to reconstruct the ABP waveform. The ABPs estimated by the new algorithm were compared with reference values obtained by cardiac catheterization or a SphygmoCor device. The results showed that there was good agreement between the estimated and directly measured pressures. The new method proposed can accurately estimate ABP, allowing this important variable to be obtained non-invasively, using standard, well validated measurement techniques. It thus has the potential to relocate ABP estimation from a research environment to more routine use in the cardiac clinic.

Falls among the elderly are a common and serious health risk that can lead to physical injuries and other complications. To promptly detect and respond to fall events, radar-based fall detection systems have gained widespread attention. In this paper, a deep learning model is proposed based on the frequency spectrum of radar signals, called the convolutional bidirectional long short-term memory (CB-LSTM) model. The introduction of the CB-LSTM model enables the fall detection system to capture both temporal sequential and spatial features simultaneously, thereby enhancing the accuracy and reliability of the detection. Extensive comparison experiments demonstrate that our model achieves an accuracy of 98.83% in detecting falls, surpassing other relevant methods currently available. In summary, this study provides effective technical support using the frequency spectrum and deep learning methods to monitor falls among the elderly through the design and experimental validation of a radar-based fall detection system, which has great potential for improving quality of life for the elderly and providing timely rescue measures.

Accurate segmentation of cardiac anatomy is a prerequisite for the diagnosis of cardiovascular disease. However, due to differences in imaging modalities and imaging devices, known as domain shift, the segmentation performance of deep learning models lacks reliability. In this paper, we propose a two-stage progressive unsupervised domain adaptation network (TSP-UDANet) to reduce domain shift when segmenting cardiac images from various sources. We alleviate the domain shift between the feature distribution of the source and target domains by introducing an intermediate domain as a bridge. The TSP-UDANet consists of three sub-networks: a style transfer sub-network, a segmentation sub-network, and a self-training sub-network. We conduct cooperative alignment of different domains at image level, feature level, and output level. Specifically, we transform the appearance of images across domains and enhance domain invariance by adversarial learning in multiple aspects to achieve unsupervised segmentation of the target modality. We validate the TSP-UDANet on the MMWHS (unpaired MRI and CT images), MS-CMRSeg (cross-modality MRI images), and M & Ms (cross-vendor MRI images) datasets. The experimental results demonstrate excellent segmentation performance and generalizability for unlabeled target modality images.

Understanding the stress patterns produced by microbubbles (MB) in blood vessels is important in enhancing the efficacy and safety of ultrasound-assisted therapy, diagnosis, and drug delivery. In this study, the wall stress produced by the non-spherical oscillation of MBs within the lumen of micro-vessels was numerically analyzed using a three-dimensional finite element method. We systematically simulated configurations containing an odd number of bubbles from three to nine, equally spaced along the long axis of the vessel, insonated at an acoustic pressure of 200 kPa. We observed that 3 MBs were sufficient to simulate the stress state of an infinite number of bubbles. As the bubble spacing increased, the interaction between them weakened to the point that they could be considered to act independently. In the relationship between stress and acoustic frequency, there were differences between the single and 3 MB cases. The stress induced by 3 MBs was greater than the single bubble case. When the bubbles were near the wall, the shear stress peak was largely independent of vessel radius, but the circumferential stress peak increased with the radius. This study offers further insight into our understanding of the magnitude and distribution of stresses produced by multiple ultrasonically excited MBs inside capillaries.

Coronary artery segmentation is an essential procedure in the computer-aided diagnosis of coronary artery disease. It aims to identify and segment the regions of interest in the coronary circulation for further processing and diagnosis. Currently, automatic segmentation of coronary arteries is often unreliable because of their small size and poor distribution of contrast medium, as well as the problems that lead to over-segmentation or omission. To improve the performance of convolutional-neural-network (CNN) based coronary artery segmentation, we propose a novel automatic method, DR-LCT-UNet, with two innovative components: the Dense Residual (DR) module and the Local Contextual Transformer (LCT) module. The DR module aims to preserve unobtrusive features through dense residual connections, while the LCT module is an improved Transformer that focuses on local contextual information, so that coronary artery-related information can be better exploited. The LCT and DR modules are effectively integrated into the skip connections and encoder-decoder of the 3D segmentation network, respectively. Experiments on our CorArtTS2020 dataset show that the dice similarity coefficient (DSC), Recall, and Precision of the proposed method reached 85.8%, 86.3% and 85.8%, respectively, outperforming 3D-UNet (taken as the reference among the 6 other chosen comparison methods), by 2.1%, 1.9%, and 2.1%.

Background and Objective: Aortic pressure (P-a) is important for the diagnosis of cardiovascular disease. However, its direct measurement is invasive, not risk-free, and relatively costly. In this paper, a new simplified Kalman filter (SKF) algorithm is employed for the reconstruction of the P-a waveform using dual peripheral artery pressure waveforms. Methods: P-a waveforms obtained in a previous study were collected from 25 patients. Simultaneously, radial and femoral pressure waveforms were generated from two simulation experiments, using transfer functions. In the first, the transfer function is a known finite impulse response; and in the second, it is derived from a tube-load model. To analyze the performance of the proposed SKF algorithm, variable amounts of noise were added to the observed output signal, to give a range of signal-to-noise ratios (SNRs). Additionally, central aortic, brachial and femoral pressure waveforms were simultaneously collected from 2 Sprague-Dawley rats and the measured and reconstructed P-a waveforms were compared. Results: The proposed SKF algorithm outperforms canonical correlation analysis (CCA), which is the current state-of-the-art blind system identification method for the non-invasive estimation of central aortic blood pressure. It is also shown that the proposed SKF algorithm is more noise-tolerant than the CCA algorithm over a wide range of SNRs. Conclusion: The simulations and animal experiments illustrate that the proposed SKF algorithm is accurate and stable in the face of low SNRs. Improved methods for estimating central blood pressure as a measure of cardiac load adds to their value as a prognostic and diagnostic tool. (C) 2022 Elsevier B.V. All rights reserved.

EDITORIAL article Front. Phys., 29 September 2022Sec. Medical Physics and Imaging Volume 10 - 2022 | https://doi.org/10.3389/fphy.2022.977624

Ballistocardiography (BCG) is considered a good alternative to HRV analysis with its non-contact and unobtrusive acquisition characteristics. However, consensus about its validity has not yet been established. In this study, 50 healthy subjects (26.2 ± 5.5 years old, 22 females, 28 males) were invited. Comprehensive statistical analysis, including Coefficients of Variation (CV), Lin’s Concordance Correlation Coefficient (LCCC), and Bland-Altman analysis (BA ratio), were utilized to analyze the consistency of BCG and ECG signals in HRV analysis. If the methods gave different answers, the worst case was taken as the result. Measures of consistency such as Mean, SDNN, LF gave good agreement (the absolute value of CV difference < 2%, LCCC > 0.99, BA ratio < 0.1) between J-J (BCG) and R-R intervals (ECG). pNN50 showed moderate agreement (the absolute value of CV difference < 5%, LCCC > 0.95, BA ratio < 0.2), while RMSSD, HF, LF/HF indicated poor agreement (the absolute value of CV difference ≥ 5% or LCCC ≤ 0.95 or BA ratio ≥ 0.2). Additionally, the R-R intervals were compared with P-P intervals extracted from the pulse wave (PW). Except for pNN50, which exhibited poor agreement in this comparison, the performances of the HRV indices estimated from the PW and the BCG signals were similar.