Left Ventricular Endocardial Wall Research Articles

An Automated Method for Detecting the Scar Tissue in the Left Ventricular Endocardial Wall Using Deep Learning Approach.

Image evaluation of scar tissue plays a significant role in the diagnosis of cardiovascular diseases. Segmentation of the scar tissue is the first step towards evaluating the morphology of the scar tissue. Then, with the use of CT images, the deep learning approach can be applied to identify possible scar tissue in the left ventricular endocardial wall. To develop an automated method for detecting the endocardial scar tissue in the left ventricular using Deep learning approach. Pixel values of the endocardial wall for each image in the sequence were extracted. Morphological operations, including defining regions of the endocardial wall of the LV where scar tissue could predominate, were performed. Convolutional Neural Networks (CNN) is a deep learning application, which allowed choosing appropriate features from delayed enhancement cardiac CT images to distinguish between endocardial scar and healthy tissues of the LV by applying pixel value-based concepts. We achieved 89.23% accuracy, 91.11% sensitivity, and 87.75% specificity in the detection of endocardial scars using the CNN-based method. Our findings reveal that the CNN-based method yielded robust accuracies in LV endocardial scar detection, which is currently the most extensively used pixel-based method of deep learning. This study provides a new direction for the assessment of scar tissue in imaging modalities and provides a potential avenue for clinical adaptations of these algorithms. Additionally this methodology, in comparison with those in the literature, provides specific advantages in its translational ability to clinical use.

Left Ventricular Trabeculations Decrease the Wall Shear Stress and Increase the Intra-Ventricular Pressure Drop in CFD Simulations.

The aim of the present study is to characterize the hemodynamics of left ventricular (LV) geometries to examine the impact of trabeculae and papillary muscles (PMs) on blood flow using high performance computing (HPC). Five pairs of detailed and smoothed LV endocardium models were reconstructed from high-resolution magnetic resonance images (MRI) of ex-vivo human hearts. The detailed model of one LV pair is characterized only by the PMs and few big trabeculae, to represent state of art level of endocardial detail. The other four detailed models obtained include instead endocardial structures measuring ≥1 mm2 in cross-sectional area. The geometrical characterizations were done using computational fluid dynamics (CFD) simulations with rigid walls and both constant and transient flow inputs on the detailed and smoothed models for comparison. These simulations do not represent a clinical or physiological scenario, but a characterization of the interaction of endocardial structures with blood flow. Steady flow simulations were employed to quantify the pressure drop between the inlet and the outlet of the LVs and the wall shear stress (WSS). Coherent structures were analyzed using the Q-criterion for both constant and transient flow inputs. Our results show that trabeculae and PMs increase the intra-ventricular pressure drop, reduce the WSS and disrupt the dominant single vortex, usually present in the smoothed-endocardium models, generating secondary small vortices. Given that obtaining high resolution anatomical detail is challenging in-vivo, we propose that the effect of trabeculations can be incorporated into smoothed ventricular geometries by adding a porous layer along the LV endocardial wall. Results show that a porous layer of a thickness of 1.2·10−2 m with a porosity of 20 kg/m2 on the smoothed-endocardium ventricle models approximates the pressure drops, vorticities and WSS observed in the detailed models.

Comparison of treadmill exercise stress cardiac MRI to stress echocardiography in healthy volunteers for adequacy of left ventricular endocardial wall visualization: A pilot study

To compare exercise stress cardiac magnetic resonance (cardiac MR) to echocardiography in healthy volunteers with respect to adequacy of endocardial visualization and confidence of stress study interpretation. Twenty-eight healthy volunteers (age 28 ± 11 years, 15 males) underwent exercise stress echo and cardiac MR one week apart assigned randomly to one test first. Stress cardiac MR was performed using an MRI-compatible treadmill; stress echo was performed as per routine protocol. Cardiac MR and echo images were independently reviewed and scored for adequacy of endocardial visualization and confidence in interpretation of the stress study. Heart rate at the time of imaging was similar between the studies. Average time from cessation of exercise to start of imaging (21 vs. 31 s, P < 0.001) and time to acquire stress images (20 vs. 51 s, P < 0.001) was shorter for cardiac MR. The number of myocardial segments adequately visualized was significantly higher by cardiac MR at rest (99.8% vs. 96.4%, P = 0.002) and stress (99.8% vs. 94.1%, P = 0.001). The proportion of subjects in whom there was high confidence in the interpretation was higher for cardiac MR than echo (96% vs. 60%, P = 0.005). Exercise stress cardiac MR to assess peak exercise wall motion is feasible and can be performed at least as rapidly as stress echo.

Bending and stretching models for LV wall motion analysis from curves and surfaces

A unified framework for motion tracking of curves and surfaces with specific applications to the left-ventricular endocardial wall is outlined. The theory considers both two-dimensional curves and three-dimensional surfaces, and in each case uses an elastic model of the object with constraints on the types of motion for tracking the movement. The emphasis of the paper is on surface motion. For surface motion, conformal stretching is measured from a system of coordinates based on the principal direction of a surface patch. In addition, an energy for bending of a 3D surface is defined and used for motion tracking. Membrane smoothing with confidences is used to optimize the initial 3D flow estimates. 4D example data from different imaging modalities of the left ventricle in motion are illustrated. Algorithms are presented, and applications to real and simulated data are included.

Quantitative assessment of regional left ventricular motion using endocardial landmarks

In this study the hypothesis is tested that the motion pattern of small anatomic landmarks, recognizable at the left ventricular endocardial border in the contrast angiocardiogram, reflects the motion of the endocardial wall. To verify this, minute metal markers were inserted in the endocardium of eight pigs with a novel retrograde transvascular approach. Marker motion was subsequently recorded with roentgen cinematography and compared with the motion of the landmarks on the endocardial contours detected from the contrast ventriculogram with an automated contour detection system. Linear regression analysis of the directions of the systolic metal marker and endocardial landmark pathways yielded a correlation coefficient of 0.86 and a standard error of the estimate of 10.3 degrees. Landmark pathways were also measured in 23 normal human left ventriculograms. Normal left ventricular endocardial wall motion during systole, as observed in the 30 degrees right anterior oblique view, is characterized by a dominant inward transverse motion of the opposite anterior and inferoposterior walls and a descent of the base toward the apex. The apex itself is almost stationary. On the basis of these observations, a widely applicable model for the assessment of left ventricular wall motion is described in mathematical terms.