近日，研究中心硕士生李天阳的一篇论文“Direct estimation of left ventricular ejection fraction via a cardiac cycle feature learning architecture”被SCI期刊Computers in Biology and Medicine接收并优先在线发表。（论文链接地址：https://doi.org/10.1016/j.compbiomed.2020.103659；代码链接地址：https://github.com/imlucaslee/Cardiac_cycle_feature_learning_architecture）

论文摘要：左室射血分数对心脏疾病的早期诊断具有重要意义。然而，由于心脏结构的高度差异性和心脏磁共振成像序列的复杂性，准确、可靠地估计左室射血分数仍然是一个巨大的挑战。目前常用的左室射血分数的测定方法主要依赖于患者的左心室容积。因此，强大的先验知识往往是必要的，这阻碍了现有方法作为临床工具的易用性。在这项研究中，我们提出了一个心脏周期特征学习架构，以实现准确和可靠的左心室射血分数测量。该架构构建了一个心动周期提取模块，该模块生成并分析光流场来获取所有图像的心动状态;一个运动特征融合和提取模块，用于对心脏序列进行时间建模;一个全连接的回归模块，用于实现直接测量。通过对145名受试者2900个磁共振成像序列的实验证明，我们的方法取得了可靠的结果，并在同一数据集上优于已有最优秀的方法。作为直接测量左室射血分数的第一个解决方案，我们提出的方法在未来的临床应用上具有巨大的潜力。

Abstract:The left ventricular ejection fraction is of significant importance for the early identification and diagnosis of cardiac disease. However, estimation of the left ventricular ejection fraction with consistently reliable and high accuracy remains a great challenge, owing to the high variability of cardiac structures and the complexity of the temporal dynamics in the cardiac magnetic resonance imaging sequences. The popular methods of left ventricular ejection fraction estimation rely on the left ventricular volume. Thus, strong prior knowledge is often necessary, impeding the ease of use of the existing methods as clinical tools. In this study, we propose a cardiac cycle feature learning architecture for achieving an accurate and reliable estimation of the left ventricular ejection fraction. The proposed method constructs a cardiac cycle extraction module that generates and analyzes an optical flow to obtain the cardiac cycle of all images, a motion feature fusion and extraction module for temporal modeling of the cardiac sequences, and a fully connected regression module for achieving a direct estimation. As compared with the current state-of-the-art method, our proposed method improves the performance by approximately 3 percent insofar as the mean absolute error. As the first solution for estimating the left ventricular ejection fraction directly, our proposed method demonstrates great potential for future clinical applications.