Patient-specific CFD simulation of intraventricular haemodynamics based on 3D ultrasound imaging

Background: The goal of this paper is to present a computational fluid dynamic (CFD) model with moving boundaries to study the intraventricular flows in a patient-specific framework. Starting from the segmentation of real-time transesophageal echocardiographic images, a CFD model including the complete left ventricle and the moving 3D mitral valve was realized. Their motion, known as a function of time from the segmented ultrasound images, was imposed as a boundary condition in an Arbitrary Lagrangian-Eulerian framework. Results: The model allowed for a realistic description of the displacement of the structures of interest and for an effective analysis of the intraventricular flows throughout the cardiac cycle. The model provides detailed intraventricular flow features, and highlights the importance of the 3D valve apparatus for the vortex dynamics and apical flow. Conclusions: The proposed method could describe the haemodynamics of the left ventricle during the cardiac cycle. The methodology might therefore be of particular importance in patient treatment planning to assess the impact of mitral valve treatment on intraventricular flow dynamics

Cardiac Motion Analysis Based on Optical Flow on Real-Time Three-Dimensional Ultrasound Data

With relatively high frame rates and the ability to acquire volume data sets with a stationary transducer, 3D ultrasound systems, based on matrix phased array transducers, provide valuable three-dimensional information, from which quantitative measures of cardiac function can be extracted. Such analyses require segmentation and visual tracking of the left ventricular endocardial border. Due to the large size of the volumetric data sets, manual tracing of the endocardial border is tedious and impractical for clinical applications. Therefore the development of automatic methods for tracking three-dimensional endocardial motion is essential. In this study, we evaluate a four-dimensional optical flow motion tracking algorithm to determine its capability to follow the endocardial border in three dimensional ultrasound data through time. The four-dimensional optical flow method was implemented using three-dimensional correlation. We tested the algorithm on an experimental open-chest dog data set and a clinical data set acquired with a Philips' iE33 three-dimensional ultrasound machine. Initialized with left ventricular endocardial data points obtained from manual tracing at end-diastole, the algorithm automatically tracked these points frame by frame through the whole cardiac cycle. Finite element surfaces were fitted through the data points obtained by both optical flow tracking and manual tracing by an experienced observer for quantitative comparison of the results. Parameterization of the finite element surfaces was performed and maps displaying relative differences between the manual and semi-automatic methods were compared. The results showed good consistency with less than 10% difference between manual tracing and optical flow estimation on 73% of the entire surface. In addition, the optical flow motion tracking algorithm greatly reduced processing time (about 94% reduction compared to human involvement per cardiac cycle) for analyzing cardiac function in three-dimensional ultrasound data sets. A displacement field was computed from the optical flow output, and a framework for computation of dynamic cardiac information is introduced. The method was applied to a clinical data set from a heart transplant patient and dynamic measurements agreed with known physiology as well as experimental results

State of the Art of Level Set Methods in Segmentation and Registration of Medical Imaging Modalities

Segmentation of medical images is an important step in various applications such as visualization, quantitative analysis and image-guided surgery. Numerous segmentation methods have been developed in the past two decades for extraction of organ contours on medical images. Low-level segmentation methods, such as pixel-based clustering, region growing, and filter-based edge detection, require additional pre-processing and post-processing as well as considerable amounts of expert intervention or information of the objects of interest. Furthermore the subsequent analysis of segmented objects is hampered by the primitive, pixel or voxel level representations from those region-based segmentation. Deformable models, on the other hand, provide an explicit representation of the boundary and the shape of the object. They combine several desirable features such as inherent connectivity and smoothness, which counteract noise and boundary irregularities, as well as the ability to incorporate knowledge about the object of interest. However, parametric deformable models have two main limitations. First, in situations where the initial model and desired object boundary differ greatly in size and shape, the model must be re-parameterized dynamically to faithfully recover the object boundary. The second limitation is that it has difficulty dealing with topological adaptation such as splitting or merging model parts, a useful property for recovering either multiple objects or objects with unknown topology. This difficulty is caused by the fact that a new parameterization must be constructed whenever topology change occurs, which requires sophisticated schemes. Level set deformable models, also referred to as geometric deformable models, provide an elegant solution to address the primary limitations of parametric deformable models. These methods have drawn a great deal of attention since their introduction in 1988. Advantages of the contour implicit formulation of the deformable model over parametric formulation include: (1) no parameterization of the contour, (2) topological flexibility, (3) good numerical stability, (4) straightforward extension of the 2D formulation to n-D. Recent reviews on the subject include papers from Suri. In this chapter we give a general overview of the level set segmentation methods with emphasize on new frameworks recently introduced in the context of medical imaging problems. We then introduce novel approaches that aim at combining segmentation and registration in a level set formulation. Finally we review a selective set of clinical works with detailed validation of the level set methods for several clinical applications

Real-time three-dimensional transthoracic echocardiography in daily practice: initial experience

<p>Abstract</p> <p>Aim of the work</p> <p>To evaluate the feasibility and possible additional value of transthoracic real-time three-dimensional echocardiography (RT3D-TTE) for the assessment of cardiac structures as compared to 2D-TTE.</p> <p>Methods</p> <p>320 patients (mean age 45 ± 8.4 years, 75% males) underwent 2D-TTE and RT3D-TTE using 3DQ-Q lab software for offline analysis. Volume quantification and functional assessment was performed in 90 patients for left ventricle and in 20 patients for right ventricle. Assessment of native (112 patients) and prosthetic (30 patients) valves morphology and functions was performed. RT3D-TTE was performed for evaluation of septal defects in 30 patients and intracardiac masses in 52 patients.</p> <p>Results</p> <p>RT3D-TTE assessment of left ventricle was feasible and reproducible in 86% of patients while for right ventricle, it was (55%). RT3D-TTE could define the surface anatomy of mitral valve optimally (100%), while for aortic and tricuspid was (88% and 81% respectively). Valve area could be planimetered in 100% for the mitral and in 80% for the aortic. RT3D-TTE provided a comprehensive anatomical and functional evaluation of prosthetic valves. RT3D-TTE enface visualization of septal defects allowed optimal assessment of shape, size, area and number of defects and evaluated the outcome post device closure. RT3D-TTE allowed looking inside the intracardiac masses through multiple sectioning, valuable anatomical delineation and volume calculation.</p> <p>Conclusion</p> <p>Our initial experience showed that the use of RT3D-TTE in the assessment of cardiac patients is feasible and allowed detailed anatomical and functional assessment of many cardiac disorders.</p

Utilization of Focus Groups to Design Curricula to Teach 3D/4D Technology

Diagnostic medical sonography is a tool utilized daily in the medical field. Currently there is a trend of moving from 2D technologies to newer, advanced 3D/4D technologies. The issue involved with adding 3D/4D technology to the echocardiography exam is how to best teach the sonographers how to become comfortable with using the newer technology. The aim of this study was to use focus groups and grounded theory as tools for curriculum development to teach cardiac sonographers 3D/4D technology to calculate left ventricular volume. The setting for this study was an academic medical center in which eight cardiac sonographers were recruited to learn how to utilize 3D technology to calculate left ventricular volumes. The sonographers were asked to participate in two focus groups, online learning modules, hands-on practice sessions, and a final hands-on session with a data set to test the effectiveness of the final educational material. The methodology utilized for this study was qualitative, with audio taped interviews in focus groups and videotaped hands-on observation of 3D phantom scanning. Grounded theory was utilized to evaluate the data collected and to develop curricula to teach sonographers how to measure left ventricular volumes. Results indicate that in order to have successful implementation of a curriculum into the laboratory, specific educational materials and hands-on practice sessions should be provided to enhance learning and understanding of 3D technology. Sonographers participating in this study defined barriers to learning 3D technology as not enough time, positioning of equipment in examination rooms, too many different uses of 3D technology, and 3D technology “experience gap.” Findings indicate that focus groups serve as a mechanism for identifying barriers to learning and designing an effective curriculum for teaching sonographers how to measure left ventricular volumes

Variational segmentation framework in prolate spheroidal coordinates for 3D real-time echocardiography

This paper presents a new formulation of a deformable model segmentation in prolate spheroidal coordinates for segmentation of 3D cardiac echocardiography data. The prolate spheroidal coordinate system enables a representation of the segmented surface with descriptors specifically adapted to the "ellipsoidal" shape of the ventricle. A simple data energy term, based on gray-level information, guides the segmentation. The segmentation framework provides a very fast and simple algorithm to evolve an initial ellipsoidal object towards the endocardial surface of the myocardium with near real-time deformations. With near real-time performance, additional constraints on landmark points, can be used interactively to prevent leakage of the surface

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