Stephen Edward Greenwald

RESEARCH, TEACHING, or OTHER INTERESTS

Biomedical Engineering, Cardiology and Cardiovascular Medicine

Scopus Publications

DIEN: A dual-factor iterative enhancement network with the global Re-calibration feature for coronary artery segmentation
Jinzhong Yang, Peng Hong, Bu Xu, Lu Wang, Lisheng Xu, Dongming Chen, Chengbao Peng, An Ping, Benqiang Yang, Xia Zhang,et al.
Elsevier BV



Automated pericardium segmentation and epicardial adipose tissue quantification from computed tomography images
Ying Wang, Ankang Wang, Lu Wang, Wenjun Tan, Lisheng Xu, Jinsong Wang, Songang Li, Jinshuai Liu, Yu Sun, Benqiang Yang,et al.
Elsevier BV



Clinical Validation of Carotid-Femoral Pulse Wave Velocity Measurement Using a Multi-Beam Laser Vibrometer: The CARDIS Study
Smriti Badhwar, Louise Marais, Hakim Khettab, Federica Poli, Yanlu Li, Patrick Segers, Soren Aasmul, Mirko de Melis, Roel Baets, Steve Greenwald,et al.
Ovid Technologies (Wolters Kluwer Health)
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 [cfPWV LDV_ECG ] and r=0.80, P <0.001 ECG-independent [cfPWV LDV_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.


Doppler Radar Sensor-Based Fall Detection Using a Convolutional Bidirectional Long Short-Term Memory Model
Zhikun Li, Jiajun Du, Baofeng Zhu, Stephen E. Greenwald, Lisheng Xu, Yudong Yao, and Nan Bao
MDPI AG
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.


COACT: Coronary artery centerline tracker
Xiaogang Li, Lianchang Ji, Rongrong Zhang, Hongrui You, Lisheng Xu, Stephen E. Greenwald, Yu Sun, Libo Zhang, and Benqiang Yang
Wiley
AbstractBackgroundThe 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 (https://slice‐doc.netlify.app) for further studies.


Patient-specific non-invasive estimation of the aortic blood pressure waveform by ultrasound and tonometry
Shuran Zhou, Kai Xu, Yi Fang, Jordi Alastruey, Samuel Vennin, Jun Yang, Junli Wang, Lisheng Xu, Xiaocheng Wang, and Steve E. Greenwald
Elsevier BV



CT-Less Whole-Body Bone Segmentation of PET Images Using a Multimodal Deep Learning Network
Nan Bao, Jiaxin Zhang, Zhikun Li, Shiyu Wei, Jiazhen Zhang, Stephen E. Greenwald, John A. Onofrey, Yihuan Lu, and Lisheng Xu
Institute of Electrical and Electronics Engineers (IEEE)



Acoustic detection of coronary artery stenosis: from in-vitro gel measurements towards a low cost diagnostic device
Vincent Adeola, Jon Reeves, Simon Shaw, Emm M. Drakakis, Kostis Petkos, and Stephen E. Greenwald
SPIE



Manipulation of Post-Prandial Hyperglycaemia in Type 2 Diabetes: An Update for Practitioners
Lina Shibib, Mo Al-Qaisi, Nicola Guess, Alexander Miras, Steve Greenwald, Marc Pelling, and Ahmed Ahmed
Informa UK Limited
Abstract 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.


TSP-UDANet: two-stage progressive unsupervised domain adaptation network for automated cross-modality cardiac segmentation
Yonghui Wang, Yifan Zhang, Lisheng Xu, Shouliang Qi, Yudong Yao, Wei Qian, Stephen E. Greenwald, and Lin Qi
Springer Science and Business Media LLC



Automatic coronary artery segmentation of CCTA images using UNet with a local contextual transformer
Qianjin Wang, Lisheng Xu, Lu Wang, Xiaofan Yang, Yu Sun, Benqiang Yang, and Stephen E. Greenwald
Frontiers Media SA
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%.


Three-dimensional numerical analysis of wall stress induced by asymmetric oscillation of microbubble trains inside micro-vessels
Jonghyok Ri, Na Pang, Shi Bai, Jialin Xu, Lisheng Xu, Songchol Ri, Yudong Yao, and Stephen E. Greenwald
AIP Publishing
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.


Improving the accuracy and robustness of carotid-femoral pulse wave velocity measurement using a simplified tube-load model
Lisheng Xu, Shuran Zhou, Lu Wang, Yang Yao, Liling Hao, Lin Qi, Yudong Yao, Hongguang Han, Ramakrishna Mukkamala, and Stephen E. Greenwald
Springer Science and Business Media LLC
AbstractArterial stiffness, as measured by pulse wave velocity, for the early non-invasive screening of cardiovascular disease is becoming ever more widely used and is an independent prognostic indicator for a variety of pathologies including arteriosclerosis. Carotid-femoral pulse wave velocity (cfPWV) is regarded as the gold standard for aortic stiffness. Existing algorithms for cfPWV estimation have been shown to have good repeatability and accuracy, however, further assessment is needed, especially when signal quality is compromised. We propose a method for calculating cfPWV based on a simplified tube-load model, which allows for the propagation and reflection of the pulse wave. In-vivo cfPWV measurements from 57 subjects and numerical cfPWV data based on a one-dimensional model were used to assess the method and its performance was compared to three other existing approaches (waveform matching, intersecting tangent, and cross-correlation). The cfPWV calculated using the simplified tube-load model had better repeatability than the other methods (Intra-group Correlation Coefficient, ICC = 0.985). The model was also more accurate than other methods (deviation, 0.13 ms−1) and was more robust when dealing with noisy signals. We conclude that the determination of cfPWV based on the proposed model can accurately and robustly evaluate arterial stiffness.


Relationship between epicardial fat volume on cardiac CT and atherosclerosis severity in three-vessel coronary artery disease: a single-center cross-sectional study
Yu Sun, Xiao-gang Li, Kai Xu, Jie Hou, Hong-rui You, Rong-rong Zhang, Miao Qi, Li-bo Zhang, Li-sheng Xu, Stephen E. Greenwald,et al.
Springer Science and Business Media LLC
AbstractBackgroundThe ideal treatment strategy for stable three-vessel coronary artery disease (CAD) patients are difficult to determine and for patients undergoing conservative treatment, imaging evidence of coronary atherosclerotic severity progression remains limited. Epicardial fat volume (EFV) on coronary CT angiography (CCTA) has been considered to be associated with coronary atherosclerosis. Therefore, this study aims to evaluate the relationship between EFV level and coronary atherosclerosis severity in three-vessel CAD.MethodsThis retrospective study enrolled 252 consecutive patients with three-vessel CAD and 252 normal control group participants who underwent CCTA between January 2018 and December 2019. A semi-automatic method was developed for EFV quantification on CCTA images, standardized by body surface area. Coronary atherosclerosis severity was evaluated and scored by the number of coronary arteries with ≥ 50% stenosis on coronary angiography. Patients were subdivided into groups on the basis of lesion severity: mild (score = 3 vessels, n = 85), moderate (3.5 vessels ≤ score < 4 vessels, n = 82), and severe (4 vessels ≤ score ≤ 7 vessels, n = 85). The independent samplet-test, analysis of variance, and logistic regression analysis were used to evaluate the associations between EFV level and severity of coronary atherosclerosis.ResultsCompared with normal controls, three-vessel CAD patients had significantly higher EFV level (65 ± 22 mL/m2vs. 48 ± 19 mL/m2;P < 0.001). In patients with three-vessel CAD, there was a progressive decline in EFV level as the score of coronary atherosclerosis severity increased, especially in those patients with a body mass index (BMI) ≥ 25 kg/m2(75 ± 21 mL/m2vs. 72 ± 22 mL/m2vs. 62 ± 17 mL/m2;P < 0.05). Multivariable regression analysis showed that both BMI (OR3.40, 95% CI 2.00–5.78,P < 0.001) and the score of coronary atherosclerosis severity (OR0.49, 95% CI 0.26–0.93,P < 0.05) were independently related to the change of EFV level.ConclusionThree-vessel CAD patients do have higher EFV level than the normal controls. While, there may be an inverse relationship between EFV level and the severity of coronary atherosclerosis in patients with three-vessel CAD.


Editorial: In vivo opto-physiological imaging
Sijung Hu, Steve Greenwald, Vincent Dwyer, Janis Spigulis, and Yutao Guo
Frontiers Media SA
This edition of Frontiers in Physics is focussed on the research topic “In Vivo OptoPhysiological Imaging” and emphasises the value of delivering high performance multifunctional in vivo imaging and its dependence on the development of sophisticated electronics, executive software and, increasingly, the application of machine learning (ML) techniques to a diverse range of signal processing algorithms. Over the last half century there has been a steady increase in the diversity of optical techniques used to distinguish normal from pathological function, and these have provided an ever-widening range of diagnostic means for monitoring treatment effectiveness. In this special edition of Frontiers in Physics, two papers from Arrigo and co-workers Arrigo et al., Arrigo et al. show how developments in optical coherence tomography (OCT) hardware and image processing are making it possible to quantify neovascularisation associated with age-related macular degeneration (AMD); thus affording new ways to investigate its pathogenesis, development and likely outcome. This troubling disease, with a prevalence in Europe of over 2% [1], is an increasing burden in countries with ageing populations [2]. The basic idea in Arrigo et al. is to understand why OCT does not reveal the full extent of neovascular lesions in AMD, especially in the type I variant. The performance of OCT in this context is also limited by differences between manufacturers and experimental protocols. Given these constraints, the aim of this study was to distinguish between high and low reflectivity lesions (as a measure of well or poorly detected blood flow) and to see the relationship between OCT performance and blood flow when compared to the gold standard of angiography, (either using fluorescein or indocyanine green, ICG). By measuring average lesion area from images of 50 eyes (beautiful images judging by the representative examples presented), the authors show that the agreement between lesion areas measured by OCT and angiography is remarkably close for type II lesions, while for type 1 lesions, the agreement is good only when using early ICG angiography OPEN ACCESS


Automatic Coronary Artery Segmentation of CCTA Images With an Efficient Feature-Fusion-and-Rectification 3D-UNet
Along Song, Lisheng Xu, Lu Wang, Bin Wang, Xiaofan Yang, Bu Xu, Benqiang Yang, and Stephen E. Greenwald
Institute of Electrical and Electronics Engineers (IEEE)
Automatic coronary artery segmentation is of great value in diagnosing coronary disease. In this paper, we propose an automatic coronary artery segmentation method for coronary computerized tomography angiography (CCTA) images based on a deep convolutional neural network. The proposed method consists of three steps. First, to improve the efficiency and effectiveness of the segmentation, a 2D DenseNet classification network is utilized to screen out the non-coronary-artery slices. Second, we propose a coronary artery segmentation network based on the 3D-UNet, which is capable of extracting, fusing and rectifying features efficiently for accurate coronary artery segmentation. Specifically, in the encoding process of the 3D-UNet network, we adapt the dense block into the 3D-UNet so that it can extract rich and representative features for coronary artery segmentation; In the decoding process, 3D residual blocks with feature rectification capability are applied to improve the segmentation quality further. Third, we introduce a Gaussian weighting method to obtain the final segmentation results. This operation can highlight the more reliable segmentation results at the center of the 3D data blocks while weakening the less reliable segmentations at the block boundary when merging the segmentation results of spatially overlapping data blocks. Experiments demonstrate that our proposed method achieves a Dice Similarity Coefficient (DSC) value of 0.826 on a CCTA dataset constructed by us. The code of the proposed method is available at https://github.com/alongsong/3D_CAS.


Estimation of central pulse wave velocity from radial pulse wave analysis
Yang Yao, Shuran Zhou, Jordi Alastruey, Liling Hao, Stephen E. Greenwald, Yuelan Zhang, Lin Xu, Lisheng Xu, and Yudong Yao
Elsevier BV



Noninvasive estimation of aortic pressure waveform based on simplified Kalman filter and dual peripheral artery pressure waveforms
Wenyan Liu, Shuo Du, Shuran Zhou, Tiemin Mei, Yuelan Zhang, Guozhe Sun, Shuang Song, Lisheng Xu, Yudong Yao, and Stephen E. Greenwald
Elsevier BV



Statistical Analysis of the Consistency of HRV Analysis Using BCG or Pulse Wave Signals
Huiying Cui, Zhongyi Wang, Bin Yu, Fangfang Jiang, Ning Geng, Yongchun Li, Lisheng Xu, Dingchang Zheng, Biyong Zhang, Peilin Lu,et al.
MDPI AG
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.


Estimation of coronary artery movement using a non-rigid registration with global-local structure preservation
Bu Xu, Benqiang Yang, Junrui Xiao, Along Song, Bin Wang, Lu Wang, Lisheng Xu, Stephen E. Greenwald, and Yudong Yao
Elsevier BV



Detection and Localization of Myocardial Infarction Based on Multi-Scale ResNet and Attention Mechanism
Yang Cao, Wenyan Liu, Shuang Zhang, Lisheng Xu, Baofeng Zhu, Huiying Cui, Ning Geng, Hongguang Han, and Stephen E. Greenwald
Frontiers Media SA
PurposeMyocardial infarction (MI) is one of the most common cardiovascular diseases, frequently resulting in death. Early and accurate diagnosis is therefore important, and the electrocardiogram (ECG) is a simple and effective method for achieving this. However, it requires assessment by a specialist; so many recent works have focused on the automatic assessment of ECG signals.MethodsFor the detection and localization of MI, deep learning models have been proposed, but the diagnostic accuracy of this approaches still need to be improved. Moreover, with deep learning methods the way in which a given result was achieved lacks interpretability. In this study, ECG data was obtained from the PhysioBank open access database, and was analyzed as follows. Firstly, the 12-lead ECG signal was preprocessed to identify each beat and obtain each heart interval. Secondly, a multi-scale deep learning model combined with a residual network and attention mechanism was proposed, where the input was the 12-lead ECG recording. Through the SENet model and the Grad-CAM algorithm, the weighting of each lead was calculated and visualized. Using existing knowledge of the way in which different types of MI gave characteristic patterns in specific ECG leads, the model was used to provisionally diagnose the type of MI according to the characteristics of each of the 12 ECG leads.ResultsTen types of MI anterior, anterior lateral, anterior septal, inferior, inferior lateral, inferior posterior, inferior posterior lateral, lateral, posterior, and posterior lateral were diagnosed. The average accuracy, sensitivity, and specificity for MI detection of all lesion types was 99.98, 99.94, and 99.98%, respectively; and the average accuracy, sensitivity, and specificity for MI localization was 99.79, 99.88, and 99.98%, respectively.ConclusionWhen compared to existing models based on traditional machine learning methods, convolutional neural networks and recurrent neural networks, the results showed that the proposed model had better diagnostic performance, being superior in accuracy, sensitivity, and specificity.


Reversal and Remission of T2DM – An Update for Practitioners
Lina Shibib, Mo Al-Qaisi, Ahmed Ahmed, Alexander D Miras, David Nott, Marc Pelling, Stephen E Greenwald, and Nicola Guess
Informa UK Limited
Abstract Over the past 50 years, many countries around the world have faced an unchecked pandemic of obesity and type 2 diabetes (T2DM). As best practice treatment of T2DM has done very little to check its growth, the pandemic of diabesity now threatens to make health-care systems economically more difficult for governments and individuals to manage within their budgets. The conventional view has been that T2DM is irreversible and progressive. However, in 2016, the World Health Organization (WHO) global report on diabetes added for the first time a section on diabetes reversal and acknowledged that it could be achieved through a number of therapeutic approaches. Many studies indicate that diabetes reversal, and possibly even long-term remission, is achievable, belying the conventional view. However, T2DM reversal is not yet a standardized area of practice and some questions remain about long-term outcomes. Diabetes reversal through diet is not articulated or discussed as a first-line target (or even goal) of treatment by any internationally recognized guidelines, which are mostly silent on the topic beyond encouraging lifestyle interventions in general. This review paper examines all the sustainable, practical, and scalable approaches to T2DM reversal, highlighting the evidence base, and serves as an interim update for practitioners looking to fill the practical knowledge gap on this topic in conventional diabetes guidelines.


A Bi-Directional LSTM Network for Estimating Continuous Upper Limb Movement from Surface Electromyography
Chenfei Ma, Chuang Lin, Oluwarotimi Williams Samuel, Weiyu Guo, Hang Zhang, Steve Greenwald, Lisheng Xu, and Guanglin Li
Institute of Electrical and Electronics Engineers (IEEE)
In human-machine interaction systems, continuous movement estimation methods occupy an important position because they are more natural and intuitive than pattern-recognition methods. Essentially, arm position is decided by the shoulder and elbow joint angles. However, the various deformations of muscles around the shoulder and elbow often lead to difficulties in sensor fixation, which results in a loss of synchronization between the surface electromyography (sEMG) signals and joint angles. In order to accurately estimate movement angles using sEMG in situations where the sEMG is not synchronized with joint angles, we utilized a bi-directional long short-term memory (Bi-LSTM) network rather than other deep learning methods to estimate non-dominant arm movements, based on the sEMG signal from the dominant arm. This estimation protocol was designed to avoid a multiplicity of sensors and to simulate more complicated loss of synchronization problems). The performance of the Bi-LSTM was compared with multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and a long short-term memory network (LSTM). The Pearson correlation coefficient (cc) between the estimated and target joint angle sequences was calculated to evaluate the performance of each neural network. The Wilcoxon signed-rank results showed that the Bi-LSTM model significantly outperformed the MLP, CNN, and LSTM models (tested with completely untrained newly recorded free movements).


Aortic pressure waveform reconstruction using a multi-channel Newton blind system identification algorithm
Wenyan Liu, Zongpeng Li, Yufan Wang, Daiyuan Song, Ning Ji, Lisheng Xu, Tiemin Mei, Yingxian Sun, and Stephen E. Greenwald
Elsevier BV



Automatic quantification of epicardial adipose tissue volume
Xiaogang Li, Yu Sun, Lisheng Xu, Stephen E. Greenwald, Libo Zhang, Rongrong Zhang, Hongrui You, and Benqiang Yang
Wiley
PurposeEpicardial fat is the adipose tissue between the serosal pericardial wall layer and the visceral layer. It is distributed mainly around the atrioventricular groove, atrial septum, ventricular septum and coronary arteries. Studies have shown that the density, thickness, volume and other characteristics of epicardial adipose tissue (EAT) are independently correlated with a variety of cardiovascular diseases. Given this association, the accurate determination of EAT volume is an essential aim of future research. Therefore, the purpose of this study was to establish a framework for fully automatic EAT segmentation and quantification in coronary computed tomography angiography (CCTA) scans.MethodsA set of 103 scans are randomly selected from our medical center. An automatic pipeline has been developed to segment and quantify the volume of EAT. First, a multi‐slice deep neural network is used to simultaneously segment the pericardium in multiple adjacent slices. Then a deformable model is employed to reduce false positive and negative regions in the segmented binary pericardial images. Finally, the pericardium mask is used to define the region of interest (ROI) and the threshold method is utilized to extract the pixels ranging from −175 Hounsfield units (HU) to −15 HU for the segmentation of EAT.ResultsThe Dice indices of the pericardial segmentation using the proposed method with respect to the manual delineation results of two radiology experts were 97.1%  0.7% and 96.9%  0.6%, respectively. The inter‐observer variability was also assessed, resulting in a Dice index of 97.0%  0.7%. For the EAT segmentation results, the Dice indices between the proposed method and the two radiology experts were 93.4%  1.5% and 93.3%  1.3%, respectively, and the same measurement between the experts themselves was 93.6%  1.9%. The Pearson’s correlation coefficients between the EAT volumes computed from the results of the proposed method and the manual delineation by the two experts were 1.00 and 0.99 and the same coefficients between the experts was 0.99.ConclusionsThis work describes the development of a fully automatic EAT segmentation and quantification method from CCTA scans and the results compare favorably with the assessments of two independent experts. The proposed method is also packaged with a graphical user interface which can be found at https://github.com/MountainAndMorning/EATSeg.

RECENT SCHOLAR PUBLICATIONS

DIEN: A dual-factor iterative enhancement network with the global Re-calibration feature for coronary artery segmentation
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Automated pericardium segmentation and epicardial adipose tissue quantification from computed tomography images
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Manipulation of Post-Prandial Hyperglycaemia in Type 2 Diabetes: An Update for Practitioners
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CT-Less Whole-Body Bone Segmentation of PET Images Using a Multimodal Deep Learning Network
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IEEE Journal of Biomedical and Health Informatics 2024



Acoustic detection of coronary artery stenosis: from in-vitro gel measurements: towards a low cost diagnostic device
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International Conference on Future of Medicine and Biological Information 2024



Clinical Validation of Carotid-Femoral Pulse Wave Velocity Measurement Using a Multi-Beam Laser Vibrometer: The CARDIS Study
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Hypertension 81 (9), 1986-1995 2024



Doppler Radar Sensor-Based Fall Detection Using a Convolutional Bidirectional Long Short-Term Memory Model
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Sensors 24 (16), 5365 2024



COACT: Coronary artery centerline tracker
X Li, L Ji, R Zhang, H You, L Xu, SE Greenwald, Y Sun, L Zhang, B Yang
Medical Physics 51 (5), 3541-3554 2024



Machine Learning Techniques for Source Localisation in Elastic Media
B Mandalia, S Greenwald, S Shaw, G Slabaugh
arXiv preprint arXiv:2404.15336 2024



Patient-specific non-invasive estimation of the aortic blood pressure waveform by ultrasound and tonometry
S Zhou, K Xu, Y Fang, J Alastruey, S Vennin, J Yang, J Wang, L Xu, ...
Computer Methods and Programs in Biomedicine 247, 108082 2024



TSP-UDANet: two-stage progressive unsupervised domain adaptation network for automated cross-modality cardiac segmentation
Y Wang, Y Zhang, L Xu, S Qi, Y Yao, W Qian, SE Greenwald, L Qi
Neural Computing and Applications 35 (30), 22189-22207 2023



Automatic coronary artery segmentation of CCTA images using UNet with a local contextual transformer
Q Wang, L Xu, L Wang, X Yang, Y Sun, B Yang, SE Greenwald
Frontiers in physiology 14, 1138257 2023



Three-dimensional numerical analysis of wall stress induced by asymmetric oscillation of microbubble trains inside micro-vessels
J Ri, N Pang, S Bai, J Xu, L Xu, S Ri, Y Yao, SE Greenwald
Physics of Fluids 35 (1) 2023



In vivo opto-physiological imaging
S Hu, S Greenwald, V Dwyer, J Spigulis, Y Guo
Frontiers in Physics 10, 977624 2022



Reversal and remission of t2dm–an update for practitioners
L Shibib, M Al-Qaisi, A Ahmed, AD Miras, D Nott, M Pelling, ...
Vascular Health and Risk Management, 417-443 2022



Estimation of central pulse wave velocity from radial pulse wave analysis
Y Yao, S Zhou, J Alastruey, L Hao, SE Greenwald, Y Zhang, L Xu, L Xu, ...
Computer Methods and Programs in Biomedicine 219, 106781 2022



Noninvasive estimation of aortic pressure waveform based on simplified Kalman filter and dual peripheral artery pressure waveforms
W Liu, S Du, S Zhou, T Mei, Y Zhang, G Sun, S Song, L Xu, Y Yao, ...
Computer Methods and Programs in Biomedicine 219, 106760 2022



Automatic coronary artery segmentation of CCTA images with an efficient feature-fusion-and-rectification 3D-UNet
A Song, L Xu, L Wang, B Wang, X Yang, B Xu, B Yang, SE Greenwald
IEEE journal of biomedical and health informatics 26 (8), 4044-4055 2022



Improving the accuracy and robustness of carotid-femoral pulse wave velocity measurement using a simplified tube-load model
L Xu, S Zhou, L Wang, Y Yao, L Hao, L Qi, Y Yao, H Han, R Mukkamala, ...
Scientific reports 12 (1), 5147 2022



Statistical analysis of the consistency of HRV analysis using BCG or pulse wave signals
H Cui, Z Wang, B Yu, F Jiang, N Geng, Y Li, L Xu, D Zheng, B Zhang, P Lu, ...
Sensors 22 (6), 2423 2022

MOST CITED SCHOLAR PUBLICATIONS

Ageing of the conduit arteries
SE Greenwald
The Journal of Pathology: A Journal of the Pathological Society of Great 2007
Citations: 807



Impaired synthesis of elastin in walls of aorta and large conduit arteries during early development as an initiating event in pathogenesis of systemic hypertension
CN Martyn, SE Greenwald
The Lancet 350 (9082), 953-955 1997
Citations: 476



Growth in utero, adult blood pressure, and arterial compliance.
CN Martyn, DJ Barker, S Jespersen, S Greenwald, C Osmond, C Berry
Heart 73 (2), 116-121 1995
Citations: 460



Improving vascular grafts: the importance of mechanical and haemodynamic properties
SE Greenwald, CL Berry
The Journal of pathology 190 (3), 292-299 2000
Citations: 415



Motion-compensated noncontact imaging photoplethysmography to monitor cardiorespiratory status during exercise
Y Sun, S Hu, V Azorin-Peris, S Greenwald, J Chambers, Y Zhu
Journal of biomedical optics 16 (7), 077010-077010-9 2011
Citations: 284



Experimental investigation of the distribution of residual strains in the artery wall
SE Greenwald, JE Moore Jr, A Rachev, TPC Kane, JJ Meister
1997
Citations: 282



Noncontact imaging photoplethysmography to effectively access pulse rate variability
Y Sun, S Hu, V Azorin-Peris, R Kalawsky, S Greenwald
Journal of biomedical optics 18 (6), 061205-061205 2013
Citations: 232



Residual strains in conduit arteries
A Rachev, SE Greenwald
Journal of biomechanics 36 (5), 661-670 2003
Citations: 223



Effects of hypertension on the static mechanical properties and chemical composition of the rat aorta
CL Berry, SE Greenwald
Cardiovascular research 10 (4), 437-451 1976
Citations: 209



Ferulic acid alleviates changes in a rat model of metabolic syndrome induced by high-carbohydrate, high-fat diet
K Senaphan, U Kukongviriyapan, W Sangartit, P Pakdeechote, ...
Nutrients 7 (8), 6446-6464 2015
Citations: 208



Validation of a device to measure arterial pulse wave velocity by a photoplethysmographic method
S Loukogeorgakis, R Dawson, N Phillips, CN Martyn, SE Greenwald
Physiological measurement 23 (3), 581 2002
Citations: 178



Use of ambient light in remote photoplethysmographic systems: comparison between a high-performance camera and a low-cost webcam
Y Sun, C Papin, V Azorin-Peris, R Kalawsky, S Greenwald, S Hu
Journal of biomedical optics 17 (3), 037005-037005 2012
Citations: 173



Structural inhomogeneity and fiber orientation in the inner arterial media
LH Timmins, Q Wu, AT Yeh, JE Moore Jr, SE Greenwald
American Journal of Physiology-Heart and Circulatory Physiology 298 (5 2010
Citations: 163



Morbid anatomy in neonates with Ebstein's anomaly of the tricuspid valve: pathophysiologic and clinical implications
DS Celermajer, SM Dodd, SE Greenwald, RKH Wyse, JE Deanfield
Journal of the American College of Cardiology 19 (5), 1049-1053 1992
Citations: 146



Curcumin improves endothelial dysfunction and vascular remodeling in 2K-1C hypertensive rats by raising nitric oxide availability and reducing oxidative stress
O Boonla, U Kukongviriyapan, P Pakdeechote, V Kukongviriyapan, ...
Nitric Oxide 42, 44-53 2014
Citations: 140



The effects of maternal protein deprivation on the fetal rat pancreas: major structural changes and their recuperation
DM Berney, M Desai, DJ Palmer, S Greenwald, A Brown, CN Hales, ...
The Journal of Pathology: A Journal of the Pathological Society of Great 1997
Citations: 126



Static mechanical properties of the developing and mature rat aorta
CL Berry, SE Greenwald, JF Rivett
Cardiovascular research 9 (5), 669-678 1975
Citations: 123



Twin-twin transfusion syndrome: the influence of intrauterine laser photocoagulation on arterial distensibility in childhood
HM Gardiner, MJO Taylor, A Karatza, T Vanderheyden, A Huber, ...
Circulation 107 (14), 1906-1911 2003
Citations: 120



A hypothesis about a mechanism for the programming of blood pressure and vascular disease in early life
CN Martyn, SE Greenwald
Clinical and Experimental Pharmacology and Physiology 28 (11), 948-951 2001
Citations: 109



Effect of age and sex on residual stress in the aorta
A Saini, C Berry, S Greenwald
Journal of vascular research 32 (6), 398-405 1995
Citations: 108