Heart disease remains a leading cause of death worldwide. Previous research has indicated that the dynamics of the cardiac left ventricle (LV) during diastolic filling may play a critical role in dictating overall cardiac health. Hence, numerous studies have aimed to predict and evaluate global cardiac health based on quantitative parameters describing LV function. However, the inherent complexity of LV diastole, in its electrical, muscular, and hemodynamic processes, has prevented the development of tools to accurately predict and diagnose heart failure at early stages, when corrective measures are most effective. In this work, it is demonstrated that major aspects of cardiac function are reflected uniquely and sensitively in the optimization of vortex formation in the blood flow during early diastole, as measured by a dimensionless numerical index. This index of optimal vortex formation correlates well with existing measures of cardiac health such as the LV ejection fraction. However, unlike existing measures, this previously undescribed index does not require patient-specific information to determine numerical index values corresponding to normal function. A study of normal and pathological cardiac health in human subjects demonstrates the ability of this global index to distinguish disease states by a straightforward analysis of noninvasive LV measurements.cardiac dysfunction ͉ left ventricle ͉ mitral flow ͉ biofluid dynamics P revious research has indicated that dynamics of the cardiac left ventricle (LV) during diastolic filling play a critical role in dictating overall cardiac health (1-8). The flow of blood from the atrium to the ventricle of the left heart during early diastolic filling, known as the E wave, has been observed in both in vivo and in vitro studies to cause the formation of a rotating fluid mass called a vortex ring (9-11, Fig. 1 a and b). This process of vortex ring formation has been studied extensively in in vitro experiments (12-15), where it has been demonstrated that fluid transport by vortex ring formation is more efficient than by a steady, straight jet of fluid (16). Furthermore, it was recently discovered that energetic constraints limit the maximum growth of individual vortex rings (14).These results suggest the possibility that vortex ring formation may be optimized in naturally occurring fluid transport processes, especially in biological systems that depend on efficient fluid transport for their survival. In ref. 17, in vivo and in vitro data were used to support the notion that, in principle, the vortex formation process can dictate optimal kinematics of any biological fluid transport system, including the human heart.In this work, we test the hypothesis that the process of vortex ring formation during early LV diastole affects cardiac health and also serves as an indicator of cardiac health. To quantify the process of vortex ring formation and its potential optimization, a quantitative index is required. The index is most useful if it is dimensionless, so that it can be com...

Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning algorithms combined with deformable models to develop and evaluate a fully automatic segmentation tool for the LV from short-axis cardiac MRI datasets. The method employs deep learning algorithms to learn the segmentation task from the ground true data. Convolutional networks are employed to automatically detect the LV chamber in MRI dataset. Stacked autoencoders are utilized to infer the shape of the LV. The inferred shape is incorporated into deformable models to improve the accuracy and robustness of the segmentation. We validated our method using 45 cardiac MR datasets taken from the MICCAI 2009 LV segmentation challenge and showed that it outperforms the state-of-the art methods. Excellent agreement with the ground truth was achieved. Validation metrics, percentage of good contours, Dice metric, average perpendicular distance and conformity, were computed as 96.69%, 0.94, 1.81mm and 0.86, versus those of 79.2% − 95.62%, 0.87-0.9, 1.76-2.97mm and 0.67-0.78, obtained by other methods, respectively.

Blood flow patterns are closely linked to the morphology and function of the cardiovascular system. These patterns reflect the exceptional adaptability of the cardiovascular system to maintain normal blood circulation under a wide range of workloads. Accurate retrieval and display of flow-related information remains a challenge because of the processes involved in mapping the flow velocity fields within specific chambers of the heart. We review the potentials and pitfalls of current approaches for blood flow visualization, with an emphasis on acquisition, display, and analysis of multidirectional flow. This document is divided into 3 sections. First, we provide a descriptive outline of the relevant concepts in cardiac fluid mechanics, including the emergence of rotation in flow and the variables that delineate vortical structures. Second, we elaborate on the main methods developed to image and visualize multidirectional cardiovascular flow, which are mainly based on cardiac magnetic resonance, ultrasound Doppler, and contrast particle imaging velocimetry, with recommendations for developing dedicated imaging protocols. Finally, we discuss the potential clinical applications and technical challenges with suggestions for further investigations.

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