A New Framework for Performing Cardiac Strain Analysis from Cine MRI Imaging in Mice
A New Framework for Performing Cardiac Strain Analysis from Cine MRI Imaging in Mice
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DOI：
10.1038/s41598-020-64206-x
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发表时间：
2020-05-07
影响因子：
4.6
通讯作者：
El-Baz, A.
中科院分区：
文献类型：
--
作者：
Hammouda, K.;Khalifa, F.;El-Baz, A.
心脏磁共振（MR）成像是评估体内心脏功能最严格的成像形式之一。应变分析可以全面评估舒张期心肌功能，这是不通过使用正常的电影成像模块测量收缩功能参数来指示的。由于小鼠的心脏尺寸小，不可能进行适当的标记成像来评估应变。在这里，我们开发了一种新的深度学习方法来自动量化心脏电影MR图像的应变。我们的框架首先使用全卷积神经网络（FCN）架构精确定位左室血池中心点。然后，从所有心脏切片中提取包含LV的感兴趣区域（ROI）。提取的roi通过一种新的FCN结构用于左室腔和心肌的分割。对于应变分析，我们开发了一种基于拉普拉斯的方法，通过求解心脏周期内每两个连续图像帧的左室轮廓之间的拉普拉斯方程来跟踪左室壁点。在跟踪之后，使用基于拉格朗日的方法进行应变估计。通过将这些分析结果与来自同一只小鼠的标记MR图像进行比较，验证了这种新的自动化应变分析系统。与标记MR成像相比，我们的算法使用cine获得的应变数据没有显着差异。此外，我们证明了我们的新算法可以确定正常和患病心脏之间的应变差异。
Cardiac magnetic resonance (MR) imaging is one of the most rigorous form of imaging to assess cardiac function in vivo. Strain analysis allows comprehensive assessment of diastolic myocardial function, which is not indicated by measuring systolic functional parameters using with a normal cine imaging module. Due to the small heart size in mice, it is not possible to perform proper tagged imaging to assess strain. Here, we developed a novel deep learning approach for automated quantification of strain from cardiac cine MR images. Our framework starts by an accurate localization of the LV blood pool center-point using a fully convolutional neural network (FCN) architecture. Then, a region of interest (ROI) that contains the LV is extracted from all heart sections. The extracted ROIs are used for the segmentation of the LV cavity and myocardium via a novel FCN architecture. For strain analysis, we developed a Laplace-based approach to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. Following tracking, the strain estimation is performed using the Lagrangian-based approach. This new automated system for strain analysis was validated by comparing the outcome of these analysis with the tagged MR images from the same mice. There were no significant differences between the strain data obtained from our algorithm using cine compared to tagged MR imaging. Furthermore, we demonstrated that our new algorithm can determine the strain differences between normal and diseased hearts.