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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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).

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.

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.

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.

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.

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.

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.

Study of correlation between wall shear stress and elasticity in atherosclerotic carotid arteries.

Objective: This paper presents the use of the texture matching method to measure the rabbit carotid artery elasticity value of the experimental group and control group respectively. It compares the experimental rabbits, when they are prompted by pathological histology to be at the period of carotid atherosclerosis fatty streaks and fiber plaques, with the control group.

Methods: We have used ultrasound linear array probe for scanning the rabbit carotid arteries.

Correlation between quantitative analysis of wall shear stress and intima-media thickness in atherosclerosis development in carotid arteries.

Background: This paper presents quantitative analysis of blood flow shear stress by measuring the carotid arterial wall shear stress (WSS) and the intima-media thickness (IMT) of experimental rabbits fed with high-fat feedstuff on a weekly basis in order to cause atherosclerosis.

Methods: This study is based on establishing an atherosclerosis model of high-fat rabbits, and measuring the rabbits' common carotid arterial WSS of the experimental group and control group on a weekly basis. Detailed analysis was performed by using WSS quantification.

Computational medical imaging and hemodynamics framework for functional analysis and assessment of cardiovascular structures.

Cardiac dysfunction constitutes common cardiovascular health issues in the society, and has been an investigation topic of strong focus by researchers in the medical imaging community. Diagnostic modalities based on echocardiography, magnetic resonance imaging, chest radiography and computed tomography are common techniques that provide cardiovascular structural information to diagnose heart defects. However, functional information of cardiovascular flow, which can in fact be used to support the diagnosis of many cardiovascular diseases with a myriad of hemodynamics performance indicators, remains unexplored to its full potential.

Computational evaluation of smoothed particle hydrodynamics for implementing blood flow modelling through CT reconstructed arteries.

Simulation of blood flow in a stenosed artery using Smoothed Particle Hydrodynamics (SPH) is a new research field, which is a particle-based method and different from the traditional continuum modelling technique such as Computational Fluid Dynamics (CFD). Both techniques harness parallel computing to process hemodynamics of cardiovascular structures. The objective of this study is to develop and test a new robust method for comparison of arterial flow velocity contours by SPH with the well-established CFD technique, and the implementation of SPH in computed tomography (CT) reconstructed arteries.

Robust estimation of carotid artery wall motion using the elasticity-based state-space approach.

The dynamics of the carotid artery wall has been recognized as a valuable indicator to evaluate the status of atherosclerotic disease in the preclinical stage. However, it is still a challenge to accurately measure this dynamics from ultrasound images. This paper aims at developing an elasticity-based state-space approach for accurately measuring the two-dimensional motion of the carotid artery wall from the ultrasound imaging sequences.