The heart in Friedreich ataxia: definition of cardiomyopathy, disease severity, and correlation with neurological symptoms.

BACKGROUND: This cross-sectional study provides a practical approach for the clinical assessment of Friedreich ataxia (FA) cardiomyopathy (FA-CM). METHODS AND RESULTS: A comprehensive cardiac assessment, including standard echocardiography, color Doppler myocardial imaging, cardiac magnetic resonance imaging, ECG, and exercise stress testing, was performed in 205 FA patients. To assess myocardial hypertrophy in FA-CM, the end-diastolic interventricular septal wall thickness (IVSTd) was found to be the best echocardiographic parameter compared with cardiac magnetic resonance imaging-determined left ventricular mass. With the use of this parameter, 4 groups of patients with FA-CM could be defined. Patients with normal values for IVSTd (31.7%) were classified as having no FA-CM. Patients with an IVSTd exceeding the predicted normal IVSTd were classified as having mild FA-CM (40%) if IVSTd exceeded the normal value by <18% or as having intermediate FA-CM (16.1%) if IVSTd exceeded the normal value by ≥18%. Patients with ejection fraction <50% were classified as having severe FA-CM (12.2%). In addition to increased myocardial mass, severe FA-CM was further characterized by dilatation of the left ventricle, reduced systolic strain rate of the posterior wall, and ECG abnormalities. Regional myocardial function correlated negatively with FA-CM groups. Younger patients had a tendency for more advanced FA-CM. Importantly, no clear correlation was found between FA-CM groups and neurological function. CONCLUSIONS: We provide and describe a readily applicable clinical grouping of the cardiomyopathy associated with FA based on echocardiographic IVSTd and ejection fraction data. Because no distinct interrelations between FA-CM and neurological status could be determined, regular follow-up of potential cardiac involvement in FA patients is essential in clinical practice.

MFP-Unet: A novel deep learning based approach for left ventricle segmentation in echocardiography.

Segmentation of the Left ventricle (LV) is a crucial step for quantitative measurements such as area, volume, and ejection fraction. However, the automatic LV segmentation in 2D echocardiographic images is a challenging task due to ill-defined borders, and operator dependence issues (insufficient reproducibility). U-net, which is a well-known architecture in medical image segmentation, addressed this problem through an encoder-decoder path. Despite outstanding overall performance, U-net ignores the contribution of all semantic strengths in the segmentation procedure. In the present study, we have proposed a novel architecture to tackle this drawback. Feature maps in all levels of the decoder path of U-net are concatenated, their depths are equalized, and up-sampled to a fixed dimension. This stack of feature maps would be the input of the semantic segmentation layer. The performance of the proposed model was evaluated using two sets of echocardiographic images: one public dataset and one prepared dataset. The proposed network yielded significantly improved results when comparing with results from U-net, dilated U-net, Unet++, ACNN, SHG, and deeplabv3. An average Dice Metric (DM) of 0.953, Hausdorff Distance (HD) of 3.49, and Mean Absolute Distance (MAD) of 1.12 are achieved in the public dataset. The correlation graph, bland-altman analysis, and box plot showed a great agreement between automatic and manually calculated volume, area, and length.

Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography.

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.

Fully Automatic 3-D-TEE Segmentation for the Planning of Transcatheter Aortic Valve Implantation.

A novel fully automatic framework for aortic valve (AV) trunk segmentation in three-dimensional (3-D) transesophageal echocardiography (TEE) datasets is proposed. The methodology combines a previously presented semiautomatic segmentation strategy by using shape-based B-spline Explicit Active Surfaces with two novel algorithms to automate the quantification of relevant AV measures. The first combines a fast rotation-invariant 3-D generalized Hough transform with a vessel-like dark tube detector to initialize the segmentation. After segmenting the AV wall, the second algorithm focuses on aligning this surface with the reference ones in order to estimate the short-axis (SAx) planes (at the left ventricular outflow tract, annulus, sinuses of Valsalva, and sinotubular junction) in which to perform the measurements. The framework has been tested in 20 3-D-TEE datasets with both stenotic and nonstenotic AVs. The initialization algorithm presented a median error of around 3 mm for the AV axis endpoints, with an overall feasibility of 90%. In its turn, the SAx detection algorithm showed to be highly reproducible, with indistinguishable results compared with the variability found between the experts' defined planes. Automatically extracted measures at the four levels showed a good agreement with the experts' ones, with limits of agreement similar to the interobserver variability. Moreover, a validation set of 20 additional stenotic AV datasets corroborated the method's applicability and accuracy. The proposed approach mitigates the variability associated with the manual quantification while significantly reducing the required analysis time (12 s versus 5 to 10 min), which shows its appeal for automatic dimensioning of the AV morphology in 3-D-TEE for the planning of transcatheter AV implantation.

Left-Atrial Segmentation From 3-D Ultrasound Using B-Spline Explicit Active Surfaces With Scale Uncoupling.

Segmentation of the left atrium (LA) of the heart allows quantification of LA volume dynamics which can give insight into cardiac function. However, very little attention has been given to LA segmentation from three-dimensional (3-D) ultrasound (US), most efforts being focused on the segmentation of the left ventricle (LV). The B-spline explicit active surfaces (BEAS) framework has been shown to be a very robust and efficient methodology to perform LV segmentation. In this study, we propose an extension of the BEAS framework, introducing B-splines with uncoupled scaling. This formulation improves the shape support for less regular and more variable structures, by giving independent control over smoothness and number of control points. Semiautomatic segmentation of the LA endocardium using this framework was tested in a setup requiring little user input, on 20 volumetric sequences of echocardiographic data from healthy subjects. The segmentation results were evaluated against manual reference delineations of the LA. Relevant LA morphological and functional parameters were derived from the segmented surfaces, in order to assess the performance of the proposed method on its clinical usage. The results showed that the modified BEAS framework is capable of accurate semiautomatic LA segmentation in 3-D transthoracic US, providing reliable quantification of the LA morphology and function.

Detailed Evaluation of Five 3D Speckle Tracking Algorithms Using Synthetic Echocardiographic Recordings.

A plethora of techniques for cardiac deformation imaging with 3D ultrasound, typically referred to as 3D speckle tracking techniques, are available from academia and industry. Although the benefits of single methods over alternative ones have been reported in separate publications, the intrinsic differences in the data and definitions used makes it hard to compare the relative performance of different solutions. To address this issue, we have recently proposed a framework to simulate realistic 3D echocardiographic recordings and used it to generate a common set of ground-truth data for 3D speckle tracking algorithms, which was made available online. The aim of this study was therefore to use the newly developed database to contrast non-commercial speckle tracking solutions from research groups with leading expertise in the field. The five techniques involved cover the most representative families of existing approaches, namely block-matching, radio-frequency tracking, optical flow and elastic image registration. The techniques were contrasted in terms of tracking and strain accuracy. The feasibility of the obtained strain measurements to diagnose pathology was also tested for ischemia and dyssynchrony.

Healing course of acute vessel wall injury after drug-eluting stent implantation assessed by optical coherence tomography.

BACKGROUND: Vessel wall injury after drug-eluting stent (DES) implantation can be characterized in detail by optical coherence tomography (OCT). Little is known about the healing course of these phenomena. METHODS AND RESULTS: In 62 lesions (62 patients), the incidence of acute vessel trauma was assessed in the stented region and the edge segments immediately after DES implantation. The healing course of these injuries was assessed at 9-month OCT follow-up using a software algorithm allowing for reliable spatial comparison of baseline and follow-up cross-sectional images. Tissue prolapse (TP) and tissue protrusions were detected in 81 and 35% of lesions, respectively. A total of 342 intra-stent dissection flaps (ISD) and 114 intra-stent dissection cavities (ISC) were visualized in 98 and 81% of lesions, respectively. Thirty-five lesions (56%) showed edge dissections (EDs). No residual TP or protrusion was observed at follow-up. Incomplete healing was seen in 8% of ISD and in 20% of ISC. For ED, a residual flap was observed in one-third of the initially dissected stent edges. Incomplete healing of acute vessel injury was associated with the presence of underlying atherosclerotic disease at baseline. Uncovered and malapposed stent struts were observed more often with incomplete healing of vessel injury at follow-up. CONCLUSIONS: Acute vessel wall trauma is highly prevalent immediately after DES implantation. Most of these injuries are minor and resolve at mid-term follow-up. Incomplete healing of ISDs seems to be associated with other OCT findings suggesting delayed arterial healing.