2,945 research outputs found
Segmentation of Myocardial Boundaries in Tagged Cardiac MRI Using Active Contours: A Gradient-Based Approach Integrating Texture Analysis
The noninvasive assessment of cardiac function is of first importance for the diagnosis of cardiovascular diseases. Among all medical scanners only a few enables radiologists to evaluate the local cardiac motion. Tagged cardiac MRI is one of them. This protocol generates on Short-Axis (SA) sequences a dark grid which is deformed in accordance with the cardiac motion. Tracking the grid allows specialists a local estimation of cardiac geometrical parameters within myocardium. The work described in this paper aims to automate the myocardial contours detection in order to optimize the detection and the tracking of the grid of tags within myocardium. The method we have developed for endocardial and epicardial contours detection is based on the use of texture analysis and active contours models. Texture analysis allows us to define energy maps more efficient than those usually used in active contours methods where attractor is often based on gradient and which were useless in our case of study, for quality of tagged cardiac MRI is very poor
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
Automatic quantitative analysis of myocardial wall motion andthickening from long- and short-axis cine mri studies
Abstract — This work presents a fully automatic method for regional myocardial contraction and motion assessment in cardiac cine MRI studies. The prerequisite of segmenting the left ventricle at all temporal phases of the study, is accomplished using a statistical model-based algorithm (3D-ASM). Subsequent functional analysis includes the assessment of LV global functional indexes like e.g. time-volume curves, ejection fraction, stroke volume and cardiac output, as well as regional function parameters, like segmental wall motion, thickening and dyssynchrony delays. I
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
Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images
Cine magnetic resonance imaging (MRI) is the current gold standard for the
assessment of cardiac anatomy and function. However, it typically only acquires
a set of two-dimensional (2D) slices of the underlying three-dimensional (3D)
anatomy of the heart, thus limiting the understanding and analysis of both
healthy and pathological cardiac morphology and physiology. In this paper, we
propose a novel fully automatic surface reconstruction pipeline capable of
reconstructing multi-class 3D cardiac anatomy meshes from raw cine MRI
acquisitions. Its key component is a multi-class point cloud completion network
(PCCN) capable of correcting both the sparsity and misalignment issues of the
3D reconstruction task in a unified model. We first evaluate the PCCN on a
large synthetic dataset of biventricular anatomies and observe Chamfer
distances between reconstructed and gold standard anatomies below or similar to
the underlying image resolution for multiple levels of slice misalignment.
Furthermore, we find a reduction in reconstruction error compared to a
benchmark 3D U-Net by 32% and 24% in terms of Hausdorff distance and mean
surface distance, respectively. We then apply the PCCN as part of our automated
reconstruction pipeline to 1000 subjects from the UK Biobank study in a
cross-domain transfer setting and demonstrate its ability to reconstruct
accurate and topologically plausible biventricular heart meshes with clinical
metrics comparable to the previous literature. Finally, we investigate the
robustness of our proposed approach and observe its capacity to successfully
handle multiple common outlier conditions
- …