Automated cardiac vortex ring identification and characterization based on Recurrent All-Pairs Field Transforms and Lagrangian Averaged Vorticity Deviation.

Automated identification of cardiac vortices is a formidable task due to the complex nature of blood flow within the heart chambers. This study proposes a novel approach that algorithmically characterizes the identification criteria of these cardiac vortices based on Lagrangian Averaged Vorticity Deviation (LAVD). For this purpose, the Recurrent All-Pairs Field Transforms (RAFT) is employed to assess the optical flow over the Phase Contrast Magnetic Resonance Imaging (PC-MRI), and to construct a continuous blood flow velocity field and reduce errors that arise from the integral process of LAVD. Additionally, Generalized Hough Transform (GHT) is applied for automated depiction of the structure of cardiac vortices. The effectiveness of this method is demonstrated and validated by the computation of the acquired cardiac flow data. The results of this comprehensive visual and analytical study show that the evolution of cardiac vortices can be effectively described and displayed, and the RAFT framework for optical flow can synthesize the in-between PC-MRIs with high accuracy. This allows cardiologists to acquire a deeper understanding of intracardiac hemodynamics and its impact on cardiac functional performance.

The synergy of cybernetical intelligence with medical image analysis for deep medicine: A methodological perspective.

CONCEPTUAL INTRODUCTION: To introduce the concept of cybernetical intelligence, deep learning, development history, international research, algorithms, and the application of these models in smart medical image analysis and deep medicine are reviewed in this paper. This study also defines the terminologies for cybernetical intelligence, deep medicine, and precision medicine. REVIEW OF METHODS: Through literature research and knowledge reorganization, this review explores the fundamental concepts and practical applications of various deep learning techniques and cybernetical intelligence by conducting extensive literature research and reorganizing existing knowledge in medical imaging and deep medicine. The discussion mainly centers on the applications of classical models in this field and addresses the limitations and challenges of these basic models. EVALUATION AND DISCUSSIONS: In this paper, the more comprehensive overview of the classical structural modules in convolutional neural networks is described in detail from the perspective of cybernetical intelligence in deep medicine. The results and data of major research contents of deep learning are consolidated and summarized. CONCLUSION: There are some problems in machine learning internationally, such as insufficient research techniques, unsystematic research methods, incomplete research depth, and incomplete evaluation research. Some suggestions are given in our review to solve the problems existing in the deep learning models. Cybernetical intelligence has proven to be a valuable and promising avenue for advancing various fields, including deep medicine and personalized medicine.

Nondimensional diabetes indices for accurate diagnosis of diabetic subjects.

OBJECTIVES: Nondimensional indices or numbers can provide a generalized approach for integrating several biological parameters into one Nondimensional Physiological Index (NDPI) that can help characterize an abnormal state associated with a particular physiological system. In this paper, we have presented four Nondimensional Physiological Indices (NDI, DBI, DIN, CGMDI) for the accurate detection of diabetes subjects. METHODOLOGY: The NDI, DBI, and DIN diabetes indices are based on the Glucose-Insulin Regulatory System (GIRS) Model, represented by the governing differential equation of blood glucose concentration response to the glucose input rate. The solutions of this governing differential equation are employed to simulate the clinical data of the Oral Glucose Tolerance Test (OGTT), and thereby evaluate the GIRS model-system parameters, which are distinctly different for the normal and diabetic subjects. Then these GIRS model parameters are combined to form singular nondimensional indices: NDI, DBI, and DIN. When these indices are applied to the OGTT clinical data, we get significantly different values for normal and diabetic subjects. The DIN diabetes index is a more objective index involving extensive clinical studies, incorporating the GIRS model parameters as well as some key clinical-data markers (based on the information gained from the model clinical simulation and parametric identification). We have then developed another CGMDI diabetes index based on the GIRS model, for the assessment of diabetic subjects using the glucose levels measured by wearable continuous glucose monitoring (CGM) devices. CLINICAL STUDY AND RESULTS: For the DIN diabetes index, our clinical study comprised of 47 subjects (26 normal and 21 diabetics). After applying DIN to the OGTT data, a Distribution Plot of DIN was developed, displaying the ranges of DIN for (i) normal (i.e., non-diabetic) subjects with no risk of becoming diabetic, (ii) normal subjects at risk of becoming diabetic, (iii) borderline diabetic subjects who can become normal (with diet control and treatment), and (iv) distinctly diabetic subjects. This distribution plot is shown to distinctly separate normal subjects from diabetic subjects and also from subjects at risk of becoming diabetic. CONCLUSIONS: In this paper, we have developed several NDPIs in the form of novel nondimensional diabetes indices for the accurate detection of diabetes and diagnosis of diabetic subjects. These nondimensional diabetes indices can enable precision medical diagnostics of diabetes, and thereby also help to develop interventional guidelines for lowering glucose levels by means of insulin infusion. The novelty of our proposed CGMDI is that it utilizes the glucose value monitored by the CGM wearable device. In the future, an app can be developed to use the CGM data in the CGMDI to enable precision diabetes detection.

Functional magnetic resonance imaging providing the brain effect mechanism of acupuncture and moxibustion treatment for depression.

The efficacy of acupuncture and moxibustion in the treatment of depression has been fully recognized internationally. However, its central mechanism is still not developed into a unified standard, and it is generally believed that the central mechanism is regulation of the cortical striatum thalamic neural pathway of the limbic system. In recent years, some scholars have applied functional magnetic resonance imaging (fMRI) to study the central mechanism and the associated brain effects of acupuncture and moxibustion treatment for depression. This study reviews the acupuncture and moxibustion treatment of depression from two aspects: (1) fMRI study of the brain function related to the acupuncture treatment of depression: different acupuncture and moxibustion methods are summarized, the fMRI technique is elaborately explained, and the results of fMRI study of the effects of acupuncture are analyzed in detail, and (2) fMRI associated "brain functional network" effects of acupuncture and moxibustion on depression, including the effects on the hippocampus, the amygdala, the cingulate gyrus, the frontal lobe, the temporal lobe, and other brain regions. The study of the effects of acupuncture on brain imaging is not adequately developed and still needs further improvement and development. The brain function networks associated with the acupuncture treatment of depression have not yet been adequately developed to provide a scientific and standardized mechanism of the effects of acupuncture. For this purpose, this study analyzes in-depth the clinical studies on the treatment of anxiety and depression by acupuncture and moxibustion, by depicting how the employment of fMRI technology provides significant imaging changes in the brain regions. Therefore, the study also provides a reference for future clinical research on the treatment of anxiety and depression.

Flow patterns in the venous sinus of pulsatile tinnitus patients with transverse sinus stenosis and underlying vortical flow as a causative factor.

BACKGROUND: Transverse sinus stenosis (TSS) is commonly found in Pulsatile Tinnitus (PT) patients. Vortex flow is prominent in venous sinus with stenosis, and so it is important to determine the distribution and strength of the vortical flow to understand its influence on the occurrence of PT. METHODS: In this study, by using computational fluid dynamics for hemodynamic analysis in patient-specific geometries based on Magnetic Resonance Imaging (MRI), we have investigated the blood flow within the venous sinus of 16 subjects with PT. We have employed both laminar and turbulent flow models for simulations, to obtain (i) streamlines of velocity distribution in the venous sinus, and (ii) pressure distributions of flow patterns in the venous sinus. Then, hemodynamic analysis in the venous sinus recirculation zone was carried out, to determine the flow patterns at the junction of transverse sinuses and sigmoid sinuses. Finally, we have proposed a new model for turbulence evaluation based on the regression analysis of anatomic and hemodynamics parameters. RESULTS: Correlation analysis between the anatomical parameters and the hemodynamic parameters has shown that stenosis at the transverse sinus was the main factor in the local hemodynamics variation in the venous sinus of patients; in this context, it is shown that vorticity can be used as a prime indicator of the severity of the stenosis function. Our results have shown a significant correlation between the vorticity and the stenotic maximum velocity (SMV) (r = 0.282, p = 0.004). Then, a parameterized prediction model is proposed to determine the vorticity in terms of flow and anatomic variables, termed as the turbulence eddy prediction model (TEP model). Our result have shown that the TEP model is sensitive to the dominant flow distribution, with a high correlation to the flow-based vorticity (r = 0.809, p = 0.009). CONCLUSIONS: The quantification of the vorticity (as both vorticity and MVV) in the downstream of TSS could be a marker for indication of turbulent energy at the transverse-sigmoid sinus, which could potentially serve as a hemodynamic marker for the functional assessment of the PT-related TSS.

GCW-UNet segmentation of cardiac magnetic resonance images for evaluation of left atrial enlargement.

BACKGROUND AND OBJECTIVE: Left atrial enlargement (LAE) is an anatomical variation of the left atrium and the result of the long-term increase of left atrial pressure. Most of the increase in stress or volume is due to potential cardiovascular disease. Studies have shown that LAE can independently predict the development of clinically significant cardiovascular disease and heart failure. If the left atrial volume is accurately measured, it will be an essential indicator of human health and an essential means for doctors to find patients' potential diseases. We can analyze the dynamic changes in the left atrial structure and analyze left atrial dilation. However, manual segmentation was inefficient and error-prone before the 3D reconstruction of the left atrium. In order to solve this problem, a convolution neural network (CNN) method based on cardiac magnetic resonance image (MRI) is proposed to automatically segment the left atrial region. METHODOLOGY: In this paper, we have proposed and developed a novel U-Net with Gaussian blur and channel weight neural network (GCW-UNet) to automatically segment the left atrial region in the MRI of a patient with LAE. After Gaussian blur, different resolutions of the MRI are obtained. High-resolution MRI clearly shows the detailed features of the left atrium, while low-resolution MRI clearly shows the overall outline of the left atrium, which can solve the problem of more minor MRI features. Adaptive channel weights can enhance the atrial segmentation capability of the network. RESULTS: Compared with the state-of-the-art left atrial segmentation methods, our CNN-based technique results in the segmentation of the left atrium being closer to the manual segmentation by an experienced radiologist. On the test datasets, the mean Dice similarity coefficient reaches 93.57%. CONCLUSION: Firstly, MRI has a small number of imaging artifacts, which results in low segmentation accuracy. Our method successfully solves the problem. Secondly, due to the high similarity between the background (the area outside the left atrium) and the foreground (the left atrium) in MRI, traditional neural networks misclassify the background as the foreground. Our GCW-Unit can address the imbalanced number of pixels between the foreground and background. Finally, after segmenting the left atrium in the MRI by GCW-Unit, we reconstructed the left atrium to model a three-dimensional heart of a patient suffering from LAE. Based on the different time frames of one heartbeat, we could present the dynamics of the left atrial structure during a cardiac cycle. This can better assist in the evaluation of LAE in heart patients.

Automated vortex identification based on Lagrangian averaged vorticity deviation in analysis of blood flow in the atrium from phase contrast MRI.

OBJECTIVE: To present and validate a method for automated identification of the Lagrangian vortices and Eulerian vortices for analyzing flow within the right atrium (RA), from phase contrast magnetic resonance imaging (PC-MRI) data. METHODOLOGY: Our proposed algorithm characterizes the trajectory integral associated with vorticity deviation and the spatial mean of vortex rings, for the Lagrangian averaged vorticity deviation (LAVD) based identification and tracking of vortex rings within the heart chamber. For this purpose, the optical flow concept was adopted to interpolate the time frames between larger discrete frames, to minimize the error caused by constructing a continuous velocity field for the integral process of LAVD. Then the Hough transform was used to automatically extract the vortex regions of interest. The computed flow data within the RA of the participants' hearts was then used to validate the performance of our proposed method. RESULTS: In the paper, illustrations are provided for derived evolution of Euler vortices and Lagrangian vortices of a healthy subject. The visualization results have shown that our proposed method can accurately identify the Euler vortices and Lagrangian vortices, in the context of measuring the vorticity and vortex volume of the vortices within the RA chamber. Then the employment of Hough transform-based automated vortex extraction has improved the robustness and scalability of the LAVD in identifying cardiac vortices. The analytical results have demonstrated that the introduction of the Horn-Schunck optical flow can more accurately synthesize the intermediate PC-MRI to construct a continuous velocity field, compared with other interpolation methods. CONCLUSION: A novel analytical framework has been developed to accurately identify the flow vortices in the RA chamber based on Horn-Schunck optical flow and Hough transform. From the obtained analytical study results, the development and changes of dominant vortices within this cardiac chamber during the cardiac cycle can be acquired. This can provide to cardiologists a deeper understanding of the hemodynamics within the heart chambers.

A Personalized Pulmonary Circulation Model to Non-Invasively Calculate Fractional Flow Reserve for Artery Stenosis Detection.

OBJECTIVE: Fractional Flow Reserve (FFR) is regarded as a fundamental index to assess pulmonary artery stenosis. The application of FFR can increase the accuracy of detection of pulmonary artery stenosis. However, the invasive examination may carry a number of physiological risks for patients. Therefore, we propose a personalized pulmonary circulation model to non-invasively calculate FFR of pulmonary artery stenosis. METHODOLOGY: We have employed a personalized pulmonary circulation model to non-invasively calculate FFR. This model combines boundary condition estimation and 3D pulmonary artery morphology reconstruction for CFD simulation. Firstly, we obtain patient-specific boundary conditions by matching cardiac output and main pulmonary artery pressure. Secondly, the 3D pulmonary artery morphology is reconstructed by semi-automatic segmentation. The CFD simulation is performed to obtain the pressure distribution in the entire pulmonary artery. Finally, the FFR in pulmonary artery stenosis is calculated as the ratio of distal pressure and proximal pressure. RESULTS: To validate our model, we compare the simulated FFR with the measured FFR by pressure guide wires examination of 20 patients. The FFR simulated by our model shows a good agreement with the measured FFR by pressure guide wires examination ( r= 0.989, 0.001). CONCLUSION: Our proposed personalized pulmonary circulation model is shown to be capable of non-invasively calculating FFR with sufficient accuracy. SIGNIFICANCE: The FFR calculated by our personalized circulation model may effectively contribute to non-invasive detection of pulmonary artery stenosis and to a comprehensive evaluation of the entire pulmonary artery vascular tree.

Effect of plaque compositions on fractional flow reserve in a fluid-structure interaction analysis.

Coronary artery disease involves the reduction of blood flow to the myocardium due to atherosclerotic plaques. The findings of myocardial ischemia may indicate severe coronary stenosis, but many studies have demonstrated a mismatch between lumen stenosis and fractional flow reserve (FFR). Recently, some clinical studies have found that the composition of atherosclerotic plaques may be a potential missing link between stenosis and ischemia. To investigate the relationship between myocardial ischemia and plaque composition, we have developed and adopted a new fluid-structure interaction (FSI) patient-specific coronary plaque model, based on computed tomography angiography data, to assess the impact on FFR as a biomechanical indicator of ischemia. A total of 180 analyses have been performed in 3D-FSI coronary artery disease models based on plaque compositions, plaque location, and stenosis degree. Hemodynamic analysis of simulation results and comparisons with other methods has been conducted to validate our models. Our results have successfully verified that the different compositions of plaques have resulted in differences in the calculated FFR. The mean FFR values with lipid plaques are [Formula: see text] as compared to the mean FFR values in lesions with fibrous plaques [Formula: see text] and calcified plaques [Formula: see text]. Besides, FFR differences between the three different plaque compositions have been shown to increase as the diameter stenosis increased. Plaque composition affects vascular stiffness and vascular dilation ability, and thereby affects the stenosis degree, resulting in abnormal FFR leading to myocardial ischemia. This interrelationship can help to diagnose the cause of high-risk coronary artery disease, leading to myocardial ischemia.

Physiologically personalized coronary blood flow model to improve the estimation of noninvasive fractional flow reserve.

PURPOSE: Coronary outlet resistance is influenced by the quantification and distribution of resting coronary blood flow. It is crucial for a more physiologically accurate estimation of fractional flow reserve (FFR) derived from computed tomography angiography (CTA), referred to as FFRCT. This study presents a physiologically personalized (PP)-based coronary blood flow model involving the outlet boundary condition (BC) and a standardized outlet truncation strategy to estimate the outlet resistance and FFRCT. METHODS: In this study, a total of 274 vessels were retrospectively collected from 221 patients who underwent coronary CTA and invasive FFR within 14 days. For FFRCT determination, we have employed a PP-based outlet BC model involving personalized physiological parameters and left ventricular mass (LVM) to quantify resting coronary blood flow. We evaluated the improvement achieved in the diagnostic performance of FFRCT by using the PP-based outlet BC model relative to the LVM-based model, with respect to the invasive FFR. Additionally, in order to evaluate the impact of the outlet truncation strategy on FFRCT, 68 vessels were randomly selected and analyzed independently by two operators, by using two different outlet truncation strategies at 1-month intervals. RESULTS: The per-vessel diagnostic performance of the PP-based outlet BC model was improved, based on invasive FFR as reference, compared to the LVM-based model: (i) accuracy/sensitivity/specificity: 91.2%/90.4%/91.8% versus 86.5%/84.6%/87.6%, for the entire dataset of 274 vessels, (ii) accuracy/sensitivity/specificity: 88.7%/82.4%/90.4% versus 82.4%/ 76.5%/84.0%, for moderately stenosis lesions. The standardized outlet truncation strategy showed good repeatability with the Kappa coefficient of 0.908. CONCLUSIONS: It has been shown that our PP-based outlet BC model and standardized outlet truncation strategy can improve the diagnostic performance and repeatability of FFRCT.

A Hybrid Approach for Cardiac Blood Flow Vortex Ring Identification Based on Optical Flow and Lagrangian Averaged Vorticity Deviation.

Objective: The measurement of cardiac blood flow vortex characteristics can help to facilitate the analysis of blood flow dynamics that regulates heart function. However, the complexity of cardiac flow along with other physical limitations makes it difficult to adequately identify the dominant vortices in a heart chamber, which play a significant role in regulating the heart function. Although the existing vortex quantification methods can achieve this goal, there are still some shortcomings: such as low precision, and ignoring the center of the vortex without the description of vortex deformation processes. To address these problems, an optical flow Lagrangian averaged vorticity deviation (Optical flow-LAVD) method is proposed. Methodology: We examined the flow within the right atrium (RA) of the participants' hearts, by using a single set of scans pertaining to a slice at two-chamber short-axis orientation. Toward adequate extraction of the vortex ring characteristics, a novel approach driven by the Lagrangian averaged vorticity deviation (LAVD) was implemented and applied to characterize the trajectory integral associated with vorticity deviation and the spatial mean of rings, by using phase-contrast magnetic resonance imaging (PC-MRI) datasets as a case study. To interpolate the time frames between every larger discrete frame and minimize the error caused by constructing a continuous velocity field for the integral process of LAVD, we implemented the optical flow as an interpolator and introduced the backward warping as an intermediate frame synthesis basis, which is then used to generate higher quality continuous velocity fields. Results: Our analytical study results showed that the proposed Optical flow-LAVD method can accurately identify vortex ring and continuous velocity fields, based on optical flow information, for yielding high reconstruction outcomes. Compared with the linear interpolation and phased-based frame interpolation methods, our proposed algorithm can generate more accurate synthesized PC-MRI. Conclusion: This study has developed a novel Optical flow-LAVD model to accurately identify cardiac vortex rings, and minimize the associated errors caused by the construction of a continuous velocity field. Our paper presents a superior vortex characteristics detection method that may potentially aid the understanding of medical experts on the dynamics of blood flow within the heart.

Lagrangian-averaged vorticity deviation of spiraling blood flow in the heart during isovolumic contraction and ejection phases.

The formation of vortex rings in the left ventricular (LV) blood flow is a mechanism for optimized blood transport from the mitral valve inlet to aortic valve outlet, and the vorticity is an important measure of a well-functioning LV. However, due to lack of quantitative methods, the process of defining the boundary of a vortex in the LV and identifying the dominant vortex components has not been studied previously. The Lagrangian-averaged vorticity deviation (LAVD) can enable us to compute the trajectory integral of the normed difference of the vorticity from its spatial mean. Therefore, in this work, we have employed LAVD to identify the Lagrangian vortices and Eulerian vortices for measuring the vortex volume and vorticity in the LV blood flow. We found that during the LV ejection period, the positive (counterclockwise) and negative (clockwise) vorticity of patients are consistently stronger than those of the healthy groups, and the counterclockwise vortex volume of healthy groups (0.84+0.26 ml) is greater than that of patients (0.55+0.28 ml) during the pre-ejection period. Then, during the middle ejection phase, the counterclockwise vortex ring volume of patients (1.89+0.36 ml) exceeds that of healthy groups (1.38+0.43 ml). Finally, during the end-ejection period, the counterclockwise vortex ring volume of healthy subjects (0.61+0.17 ml) is the same as that of patients (0.60+0.19 ml). The results presented in this paper can provide new insights into the blood flow patterns within the LV. It can accurately indicate the role of vortices and vorticity values in intra-LV flow, and portray how cardiomyopathy (and its distorted contractile mechanism) can affect intra-LV flow patterns and mitigate adequate LV outflow.

Left Atrial Remodeling Mechanisms Associated with Atrial Fibrillation.

Heart disease has always been one of the important diseases that endanger health and cause death. Therefore, it is particularly important to understand left atrium reconstruction and atrial fibrillation before heart image processing. The purpose of this paper is to provide an important review of the mechanisms of left atrial remodeling (LAR) associated with atrial fibrillation (AF). LAR refers to the spectrum of pathophysiological changes in (i) atrial structure and physiological function, and (ii) electric, ionic, and molecular milieu of the LA, in response to stresses imposed by conditions such as hypertension, myocardial ischemia, autonomic denervation and congestive heart failure. The main mechanisms of LAR include electrical remodeling, structural remodeling, metabolic remodeling, autonomic remodeling, neurohormones and inflammation, and other influencing factors. LAR is not only the basic mechanism of AF and heart failure, but also the pathophysiological basis of its progression. In clinical practice, AF is the most common persistent arrhythmia, and is believed to be the result of a combination of mechanisms that have triggers and maintenance mechanisms, including spontaneous ectopic pacing and multiple wavelet reentry. While LA electrophysiological, structural, and ultra-structural changes trigger AF, in turn, AF alters the LA electrical and structural properties that promote its maintenance and recurrence. Chronic AF leads to extensive changes in atrial cellular substructures, including loss of myofibrils, accumulation of glycogen, changes in mitochondrial shape and size, fragmentation of sarcoplasmic reticulum, and dispersion of nuclear chromatin. Electrical remodeling and structural remodeling of the atria during AF, involving structural changes and functional impairment of the left atrium, can lead to serious decline in left ventricular function and severe heart failure. Therefore, LAR and AF are inter-activating phenomena, and the resulting complications can cause serious disabling and fatal events. In this paper, we present (i) the mechanisms of LAR, in the form of structural, electrical, metabolic, and neurohormonal changes, and (ii) their interactive roles in initiating and maintaining AF. These in-depth understanding of the atrial remodeling mechanisms can in turn provide useful insights into the treatment of AF and heart failure.

Generating wall shear stress for coronary artery in real-time using neural networks: Feasibility and initial results based on idealized models.

Computational fluid dynamics (CFD) and medical imaging can be integrated to derive some important hemodynamic parameters such as wall shear stress (WSS). However, CFD suffers from a relatively long computational time that usually varies from dozens of minutes to hours. Machine learning is a popular tool that has been applied to many fields, and it can predict outcomes fast and even instantaneously in most applications. This study aims to use machine learning as an alternative to CFD for generating hemodynamic parameters in real-time diagnosis during medical examinations. To perform the feasibility study, we used CFD to model the blood flow in 2000 idealized coronary arteries, and the calculated WSS values in these models were used as the dataset for training and testing. The preparation of the dataset was automated by scripts programmed in Python, and OpenFOAM was used as the CFD solver. We have explored multivariate linear regression, multi-layer perceptron, and convolutional neural network architectures to generate WSS values from coronary artery geometry directly without CFD. These architectures were implemented in TensorFlow 2.0. Our results showed that these algorithms were able to generate results in less than 1 s, proving its capability in real-time applications, in terms of computational time. Based on the accuracy, convolutional neural network outperformed the other architectures with a normalized mean absolute error of 2.5%. Although this study is based on idealized models, to the best of our knowledge, it is the first attempt to predict WSS in a stenosed coronary artery using machine learning approaches.

Direct Quantification of Coronary Artery Stenosis Through Hierarchical Attentive Multi-View Learning.

Quantification of coronary artery stenosis on X-ray angiography (XRA) images is of great importance during the intraoperative treatment of coronary artery disease. It serves to quantify the coronary artery stenosis by estimating the clinical morphological indices, which are essential in clinical decision making. However, stenosis quantification is still a challenging task due to the overlapping, diversity and small-size region of the stenosis in the XRA images. While efforts have been devoted to stenosis quantification through low-level features, these methods have difficulty in learning the real mapping from these features to the stenosis indices. These methods are still cumbersome and unreliable for the intraoperative procedures due to their two-phase quantification, which depends on the results of segmentation or reconstruction of the coronary artery. In this work, we are proposing a hierarchical attentive multi-view learning model (HEAL) to achieve a direct quantification of coronary artery stenosis, without the intermediate segmentation or reconstruction. We have designed a multi-view learning model to learn more complementary information of the stenosis from different views. For this purpose, an intra-view hierarchical attentive block is proposed to learn the discriminative information of stenosis. Additionally, a stenosis representation learning module is developed to extract the multi-scale features from the keyframe perspective for considering the clinical workflow. Finally, the morphological indices are directly estimated based on the multi-view feature embedding. Extensive experiment studies on clinical multi-manufacturer dataset consisting of 228 subjects show the superiority of our HEAL against nine comparing methods, including direct quantification methods and multi-view learning methods. The experimental results demonstrate the better clinical agreement between the ground truth and the prediction, which endows our proposed method with a great potential for the efficient intraoperative treatment of coronary artery disease.

Coronary arteries hemodynamics: effect of arterial geometry on hemodynamic parameters causing atherosclerosis.

Coronary arteries have high curvatures, and hence, flow through them causes disturbed flow patterns, resulting in stenosis and atherosclerosis. This in turn decreases the myocardial flow perfusion, causing myocardial ischemia and infarction. Therefore, in order to understand the mechanisms of these phenomena caused by high curvatures and branching of coronary arteries, we have conducted elaborate hemodynamic analysis for both (i) idealized coronary arteries with geometrical parameters representing realistic curvatures and stenosis and (ii) patient-specific coronary arteries with stenoses. Firstly, in idealized coronary arteries with approximated realistic arterial geometry representative of their curvedness and stenosis, we have computed the hemodynamic parameters of pressure drop, wall shear stress (WSS) and wall pressure gradient (WPG), and their association with the geometrical parameters of curvedness and stenosis. Secondly, we have similarly determined the wall shear stress and wall pressure gradient distributions in four patient-specific curved stenotic right coronary arteries (RCAs), which were reconstructed from medical images of patients diagnosed with atherosclerosis and stenosis; our results show high WSS and WPG regions at the stenoses and inner wall of the arterial curves. This paper provides useful insights into the causative mechanisms of the high incidence of atherosclerosis in coronary arteries. It also provides guidelines for how simulation of blood flow in patient's coronary arteries and determination of the hemodynamic parameters of WSS and WPG can provide a medical assessment of the risk of development of atherosclerosis and plaque formation, leading to myocardial ischemia and infarction. The novelty of our paper is in our showing how in actual coronary arteries (based on their CT imaging) curvilinearity and narrowing complications affect the computed WSS and WPG, associated with risk of atherosclerosis. This is very important for cardiologists to be able to properly take care of their patients and provide remedial measures before coronary complications lead to myocardial infarctions and necessitate stenting or coronary bypass surgery. We want to go one step further and provide clinical application of our research work. For that, we are offering to cardiologists worldwide to carry out hemodynamic analysis of the medically imaged coronary arteries of their patients and compute the values of the hemodynamic parameters of WSS and WPG, so as to provide them an assessment of the risk of atherosclerosis for their patients. Graphical abstract Theme and aims: Coronary arteries have high curvatures, and hence flow through them causes disturbed flow patterns, resulting in stenosis and atherosclerosis. This in turn decreases the myocardial flow perfusion, causing myocardial ischemia and infarction. Therefore, in order to understand the mechanisms of these phenomena caused by high curvatures and branching of coronary arteries, we have conducted elaborate hemodynamic analysis for both (i) idealized coronary arteries with geometrical parameters representing curvatures and stenosis, and (ii) patient-specific coronary arteries with stenoses. Methods and results: Firstly, in idealized coronary arteries with approximated realistic arterial geometry representative of their curvedness and stenosis, we have computed the hemodynamic parameters of pressure drop, wall shear stress (WSS) and wall pressure gradient (WPG), and their association with the geometrical parameters of curvedness and stenosis. Then, we have determined the wall shear stress and wall pressure gradient distributions in four patient-specific curved stenotic right coronary arteries (RCAs), that were reconstructed from medical images of patients diagnosed with atherosclerosis and stenosis, as illustrated in Figure 1, in which the locations of the stenoses are highlighted by arrows. Figure 1: Three-dimensional CT visualization of arteries in patients with suspected coronary disease. The arteries can be seen as a combination of various curved segments with stenoses at unspecific locations highlighted by arrows. Our results show high WSS and WPG regions at the stenoses and inner wall of the arterial curves, as depicted in Figure 2. Therein, the encapsulations show (i) high WSS, and (ii) high WPG regions at the stenosis and inner wall of the arterial curves. Figure 2: WSS and WPG surface plot of realistic arteries (a), (b), (c) and (d), wherein the small squared parts are enlarged to show the detailed localized contour plots at the stenotic regions. Therein, the circular encapsulations show (i) high WSS and (ii) high WPG regions at the stenosis and inner wall of the arterial curves. Conclusion and novelty: This paper provides useful insights into the causative mechanisms of the high incidence of atherosclerosis in coronary arteries. It also provides guidelines for how simulation of blood flow in patient coronary arteries and determination of the hemodynamic parameters of WSS and WPG can provide a medical assessment of the risk of development of atherosclerosis and plaque formation, leading to myocardial ischemia and infarction. The novelty of our paper is our showing how in actual coronary arteries (based on their CT imaging), curvilinearity and narrowing complications affect the computed WSS and WPG associated with risk of atherosclerosis. This is very important for cardiologists to be able to properly take care of their patients and provide remedial measures before coronary complications lead to myocardial infarctions and necessitate stenting or coronary bypass surgery.

Direct delineation of myocardial infarction without contrast agents using a joint motion feature learning architecture.

Changes in mechanical properties of myocardium caused by a infarction can lead to kinematic abnormalities. This phenomenon has inspired us to develop this work for delineation of myocardial infarction area directly from non-contrast agents cardiac MR imaging sequences. The main contribution of this work is to develop a new joint motion feature learning architecture to efficiently establish direct correspondences between motion features and tissue properties. This architecture consists of three seamless connected function layers: the heart localization layers can automatically crop the region of interest (ROI) sequences involving the left ventricle from the cardiac MR imaging sequences; the motion feature extraction layers, using long short-term memory-recurrent neural networks, a) builds patch-based motion features through local intensity changes between fixed-size patch sequences (cropped from image sequences), and b) uses optical flow techniques to build image-based features through global intensity changes between adjacent images to describe the motion of each pixel; the fully connected discriminative layers can combine two types of motion features together in each pixel and then build the correspondences between motion features and tissue identities (that is, infarct or not) in each pixel. We validated the performance of our framework in 165 cine cardiac MR imaging datasets by comparing to the ground truths manually segmented from delayed Gadolinium-enhanced MR cardiac images by two radiologists with more than 10 years of experience. Our experimental results show that our proposed method has a high and stable accuracy (pixel-level: 95.03%) and consistency (Kappa statistic: 0.91; Dice: 89.87%; RMSE: 0.72  mm; Hausdorff distance: 5.91  mm) compared to manual delineation results. Overall, the advantage of our framework is that it can determine the tissue identity in each pixel from its motion pattern captured by normal cine cardiac MR images, which makes it an attractive tool for the clinical diagnosis of infarction.

Robust Segmentation of Intima-Media Borders With Different Morphologies and Dynamics During the Cardiac Cycle.

Segmentation of carotid intima-media (IM) borders from ultrasound sequences is challenging because of unknown image noise and varying IM border morphologies and/or dynamics. In this paper, we have developed a state-space framework to sequentially segment the carotid IM borders in each image throughout the cardiac cycle. In this framework, an ${\mathrm{H}}_{\mathrm{\infty }}$ filter is used to solve the state-space equations, and a grayscale-derivative constraint snake is used to provide accurate measurements for the ${\mathrm{H}}_{\mathrm{\infty }}$ filter. We have evaluated the performance of our approach by comparing our segmentation results to the manually traced contours of ultrasound image sequences of three synthetic models and 156 real subjects from four medical centers. The results show that our method has a small segmentation error (lumen intima, LI: 53 $\pm\, 67\;{\mathrm{\mu }}$m; media-adventitia, MA: 57 $\pm\, 63\;{\mathrm{\mu }}$m) for synthetic and real sequences of different image characteristics, and also agrees well with the manual segmentation (LI: bias = 1.44 ${\mathrm{\mu }}$m; MA: bias = $-$3.38 ${\mathrm{\mu }}$m). Our approach can robustly segment the carotid ultrasound sequences with various IM border morphologies, dynamics, and unknown image noise. These results indicate the potential of our framework to segment IM borders for clinical diagnosis.

Assessment of boundary conditions for CFD simulation in human carotid artery.

Computational fluid dynamics (CFD) is an increasingly used method for investigation of hemodynamic parameters and their alterations under pathological conditions, which are important indicators for diagnosis of cardiovascular disease. In hemodynamic simulation models, the employment of appropriate boundary conditions (BCs) determines the computational accuracy of the CFD simulation in comparison with pressure and velocity measurements. In this study, we have first assessed the influence of inlet boundary conditions on hemodynamic CFD simulations. We selected two typical patients suspected of carotid artery disease, with mild stenosis and severe stenosis. Both patients underwent digital subtraction angiography (DSA), magnetic resonance angiography, and the invasive pressure guide wire measured pressure profile. We have performed computational experiments to (1) study the hemodynamic simulation outcomes of distributions of wall shear stress, pressure, pressure gradient and (2) determine the differences in hemodynamic performances caused by inlet BCs derived from DSA and Womersley analytical solution. Our study has found that the difference is related to the severity of the stenosis; the greater the stenosis, the more the difference ensues. Further, in our study, the two typical subjects with invasively measured pressure profile and thirty subjects with ultrasound Doppler velocimeter (UDV) measurement served as the criteria to evaluate the hemodynamic outcomes of wall shear stress, pressure, pressure gradient and velocity due to different outlet BCs based on the Windkessel model, structured-tree model, and fully developed flow model. According to the pressure profiles, the fully developed model appeared to have more fluctuations compared with the other two models. The Windkessel model had more singularities before convergence. The three outlet BCs models also showed good correlation with the UDV measurement, while the Windkessel model appeared to be slightly better ([Formula: see text]). The structured-tree model was seen to have the best performance in terms of available computational cost and accuracy. The results of our numerical simulation and the good correlation with the computed pressure and velocity with their measurements have highlighted the effectiveness of CFD simulation in patient-specific human carotid artery with suspected stenosis.