2,489 research outputs found
Estimating prognosis in patients with acute myocardial infarction using personalized computational heart models
Biomechanical computational models have potential prognostic utility in patients after an acute ST-segment–elevation myocardial infarction (STEMI). In a proof-of-concept study, we defined two groups (1) an acute STEMI group (n = 6, 83% male, age 54 ± 12 years) complicated by left ventricular (LV) systolic dysfunction; (2) an age- and sex- matched hyper-control group (n = 6, 83% male, age 46 ± 14 years), no prior history of cardiovascular disease and normal systolic blood pressure (SBP < 130 mmHg). Cardiac MRI was performed in the patients (2 days & 6 months post-STEMI) and the volunteers, and biomechanical heart models were synthesized for each subject. The candidate parameters included normalized active tension (ATnorm) and active tension at the resting sarcomere length (Treq, reflecting required contractility). Myocardial contractility was inversely determined from personalized heart models by matching CMR-imaged LV dynamics. Compared with controls, patients with recent STEMI exhibited increased LV wall active tension when normalized by SBP. We observed a linear relationship between Treq 2 days post-MI and global longitudinal strain 6 months later (r = 0.86; p = 0.03). Treq may be associated with changes in LV function in the longer term in STEMI patients complicated by LV dysfunction. Further studies seem warranted
A coupled mitral valve -- left ventricle model with fluid-structure interaction
Understanding the interaction between the valves and walls of the heart is
important in assessing and subsequently treating heart dysfunction. With
advancements in cardiac imaging, nonlinear mechanics and computational
techniques, it is now possible to explore the mechanics of valve-heart
interactions using anatomically and physiologically realistic models. This
study presents an integrated model of the mitral valve (MV) coupled to the left
ventricle (LV), with the geometry derived from in vivo clinical magnetic
resonance images. Numerical simulations using this coupled MV-LV model are
developed using an immersed boundary/finite element method. The model
incorporates detailed valvular features, left ventricular contraction,
nonlinear soft tissue mechanics, and fluid-mediated interactions between the MV
and LV wall. We use the model to simulate the cardiac function from diastole to
systole, and investigate how myocardial active relaxation function affects the
LV pump function. The results of the new model agree with in vivo measurements,
and demonstrate that the diastolic filling pressure increases significantly
with impaired myocardial active relaxation to maintain the normal cardiac
output. The coupled model has the potential to advance fundamental knowledge of
mechanisms underlying MV-LV interaction, and help in risk stratification and
optimization of therapies for heart diseases.Comment: 25 pages, 6 figure
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
.Blood flow patterns estimation in the left ventricle with low-rate 2D and 3D dynamic contrast-enhanced ultrasound
a b s t r a c t Background and Objective : Left ventricle (LV) dysfunction always occurs at early heart-failure stages, pro- ducing variations in the LV flow patterns. Cardiac diagnostics may therefore benefit from flow-pattern analysis. Several visualization tools have been proposed that require ultrafast ultrasound acquisitions. However, ultrafast ultrasound is not standard in clinical scanners. Meanwhile techniques that can handle low frame rates are still lacking. As a result, the clinical translation of these techniques remains limited, especially for 3D acquisitions where the volume rates are intrinsically low. Methods : To overcome these limitations, we propose a novel technique for the estimation of LV blood velocity and relative-pressure fields from dynamic contrast-enhanced ultrasound (DCE-US) at low frame rates. Different from other methods, our method is based on the time-delays between time-intensity curves measured at neighbor pixels in the DCE-US loops. Using Navier-Stokes equation, we regularize the obtained velocity fields and derive relative-pressure estimates. Blood flow patterns were characterized with regard to their vorticity, relative-pressure changes (dp/dt) in the LV outflow tract, and viscous energy loss, as these reflect the ejection efficiency. Results : We evaluated the proposed method on 18 patients (9 responders and 9 non-responders) who un- derwent cardiac resynchronization therapy (CRT). After CRT, the responder group evidenced a significant (p < 0.05) increase in vorticity and peak dp/dt, and a non-significant decrease in viscous energy loss. No significant difference was found in the non-responder group. Relative feature variation before and after CRT evidenced a significant difference (p < 0.05) between responders and non-responders for vorticity and peak dp/dt. Finally, the method feasibility is also shown with 3D DCE-US. Conclusions : Using the proposed method, adequate visualization and quantification of blood flow patterns are successfully enabled based on low-rate DCE-US of the LV, facilitating the clinical adoption of the method using standard ultrasound scanners. The clinical value of the method in the context of CRT is also shown
Segmentation of Patient-Specific 3D Cardiac Magnetic Resonance Images of Human Right Ventricle
Right Ventricular (RV) dysfunction is a common cause of heart failure in patients with congenital heart defects and often leads to impaired functional capacity and premature death. 3D cardiac magnetic resonance imaging (CMR)-based RV/LV combination models with fluid-structure interactions have been introduced to perform mechanical analysis and optimize RV remodeling surgery. Obtaining accurate RV/LV morphology is a very important step in the model-constructing process. A semi-automatic segmentation process was introduced in this project to obtain RV/LV/Valve geometry from patient-specific 3D CMR images. A total of 420 contour results were obtained from one patient CMRI data using QMASS software package at Department of Cardiology of Children¡¯s hospital. The digital contour data were automatically acquired using a self-developed program written in MATLAB. 3D visualizations of the RV/LV combination model at different phases throughout the cardiac cycle were presented and RV/LV volume curves were given showing the volume variation based on digital contour data under MATLAB environment. For the patient considered, the RV stoke volume (SV) is 190.8 ml (normal value is 60-136 ml) and ejection fraction is 43.5% (normal value is 47%-63%). In future work, the surgical, CMR imaging and computational modeling will be integrated together to optimize patch design and RV volume reduction surgery procedures to maximize recovery of RV cardiac function
A Non-Rigid Registration Method for Analyzing Myocardial Wall Motion for Cardiac CT Images
Cardiac resynchronization therapy (CRT) has a high percentage of non-responders. Successfully locating the optimal location for CRT lead placement on a priori images can increase efficiency in procedural preparation and execution and could potentially increase the rate of CRT responders.
Registration has been used in the past to assess the motion of medical images. Specifically, one method of non-rigid registration has been utilized to assess the motion of left ventricular MR cardiac images. As CT imaging is often performed as part of resynchronization treatment planning and is a fast and accessible means of imaging, extending this registration method to assessing left ventricular motion of CT images could provide another means of reproducible contractility assessment.
This thesis investigates the use of non-rigid registration to evaluate the myocardium motion in multi-phase multi-slice computed tomography (MSCT) cardiac imaging for the evaluation of mechanical contraction of the left ventricle
Use of mathematical modeling to study pressure regimes in normal and Fontan blood flow circulations
We develop two mathematical lumped parameter models for blood pressure
distribution in the Fontan blood flow circulation: an ODE based spatially
homogeneous model and a PDE based spatially inhomogeneous model. We present
numerical simulations of the cardiac pressure-volume cycle and study the effect
of pulmonary resistance on cardiac output. We analyze solutions of two
initial-boundary value problems for a non-linear parabolic partial differential
equation (PDE models) with switching in the time dynamic boundary conditions
which model blood pressure distribution in the cardiovascular system with and
without Fontan surgery. We also obtain necessary conditions for parameter
values of the PDE models for existence and uniqueness of non-negative bounded
periodic solutions.Comment: 32 pages, 6 figures, 1 tabl
Dynamic finite-strain modelling of the human left ventricle in health and disease using an immersed boundary-finite element method
Detailed models of the biomechanics of the heart are important both for developing improved interventions for patients with heart disease and also for patient risk stratification and treatment planning. For instance, stress distributions in the heart affect cardiac remodelling, but such distributions are not presently accessible in patients. Biomechanical models of the heart offer detailed three-dimensional deformation, stress and strain fields that can supplement conventional clinical data. In this work, we introduce dynamic computational models of the human left ventricle (LV) that are derived from clinical imaging data obtained from a healthy subject and from a patient with a myocardial infarction (MI). Both models incorporate a detailed invariant-based orthotropic description of the passive elasticity of the ventricular myocardium along with a detailed biophysical model of active tension generation in the ventricular muscle. These constitutive models are employed within a dynamic simulation framework that accounts for the inertia of the ventricular muscle and the blood that is based on an immersed boundary (IB) method with a finite element description of the structural mechanics. The geometry of the models is based on data obtained non-invasively by cardiac magnetic resonance (CMR). CMR imaging data are also used to estimate the parameters of the passive and active constitutive models, which are determined so that the simulated end-diastolic and end-systolic volumes agree with the corresponding volumes determined from the CMR imaging studies. Using these models, we simulate LV dynamics from end-diastole to end-systole. The results of our simulations are shown to be in good agreement with subject-specific CMR-derived strain measurements and also with earlier clinical studies on human LV strain distributions
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