Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3DLGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0.27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF.

PurposeAtrial fibrillation (AF) is the most common heart rhythm disorder and causes considerable morbidity and mortality, resulting in a large public health burden that is increasing as the population ages. It is associated with atrial fibrosis, the amount and distribution of which can be used to stratify patients and to guide subsequent electrophysiology ablation treatment. Atrial fibrosis may be assessed noninvasively using late gadolinium‐enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is visualized as a region of signal enhancement. However, manual segmentation of the heart chambers and of the atrial scar tissue is time consuming and subject to interoperator variability, particularly as image quality in AF is often poor. In this study, we propose a novel fully automatic pipeline to achieve accurate and objective segmentation of the heart (from MRI Roadmap data) and of scar tissue within the heart (from LGE MRI data) acquired in patients with AF.MethodsOur fully automatic pipeline uniquely combines: (a) a multiatlas‐based whole heart segmentation (MA‐WHS) to determine the cardiac anatomy from an MRI Roadmap acquisition which is then mapped to LGE MRI, and (b) a super‐pixel and supervised learning based approach to delineate the distribution and extent of atrial scarring in LGE MRI. We compared the accuracy of the automatic analysis to manual ground truth segmentations in 37 patients with persistent long‐standing AF.ResultsBoth our MA‐WHS and atrial scarring segmentations showed accurate delineations of cardiac anatomy (mean Dice = 89%) and atrial scarring (mean Dice = 79%), respectively, compared to the established ground truth from manual segmentation. In addition, compared to the ground truth, we obtained 88% segmentation accuracy, with 90% sensitivity and 79% specificity. Receiver operating characteristic analysis achieved an average area under the curve of 0.91.ConclusionCompared with previously studied methods with manual interventions, our innovative pipeline demonstrated comparable results, but was computed fully automatically. The proposed segmentation methods allow LGE MRI to be used as an objective assessment tool for localization, visualization, and quantitation of atrial scarring and to guide ablation treatment.

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PEARS is effective in stabilizing the aortic root and preventing its dilatation. It is a viable alternative for prevention of aortic root dissection in Marfan patients.

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Patients with NIDC showed increased proximal aortic stiffness, which relates to LV systolic and diastolic function and exercise capacity. The echocardiographic assessment of the regional aorta PWV seems to be clinically important.

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AimsTo assess cardiac gene expression in peripheral blood cells of patients with idiopathic dilated cardiomyopathy (IDCM) and its relationship to echocardiographic left ventricular (LV) function. Methods and resultsA complete echocardiographic study and blood sampling were performed in 65 consecutive stable IDCM patients with LV ejection fraction (LVEF) 31.76 + 10.07% and chronic mild to moderate heart failure (NYHA functional class II to III) for ≥9 months. Blood samples from 19 healthy individuals were included for comparison. Transcript levels of myocardin, GATA4, alpha-and beta-myosin heavy chain (MHC), sarcoplasmic reticulum calcium ATPase 2 (SERCA2), and phospholamban were determined by quantitative real-time reverse transcription-polymerase chain reaction. Myocardin (24.88 + 4.93 vs. 3.98 + 1.12, P ¼ 0. ConclusionOur data indicate that cardiac gene expression alterations in peripheral blood cells of IDCM patients may reflect alterations in LV function, whereas the presence of AF may be associated with increased SERCA2 levels in these patients.--