Instituto do Coracao HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil; Escola Politecnica da Universidade de Sao Paulo, Sao Paulo, SP, Brazil.
Instituto do Coracao HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil.
Comput Med Imaging Graph. 2023 Sep;108:102283. doi: 10.1016/j.compmedimag.2023.102283. Epub 2023 Aug 1.
Strain represents the quantification of regional tissue deformation within a given area. Myocardial strain has demonstrated considerable utility as an indicator for the assessment of cardiac function. Notably, it exhibits greater sensitivity in detecting subtle myocardial abnormalities compared to conventional cardiac function indices, like left ventricle ejection fraction (LVEF). Nonetheless, the estimation of strain poses considerable challenges due to the necessity for precise tracking of myocardial motion throughout the complete cardiac cycle. This study introduces a novel deep learning-based pipeline, designed to automatically and accurately estimate myocardial strain from three-dimensional (3D) cine-MR images. Consequently, our investigation presents a comprehensive pipeline for the precise quantification of local and global myocardial strain. This pipeline incorporates a supervised Convolutional Neural Network (CNN) for accurate segmentation of the cardiac muscle and an unsupervised CNN for robust left ventricle motion tracking, enabling the estimation of strain in both artificial phantoms and real cine-MR images. Our investigation involved a comprehensive comparison of our findings with those obtained from two commonly utilized commercial software in this field. This analysis encompassed the examination of both intra- and inter-user variability. The proposed pipeline exhibited demonstrable reliability and reduced divergence levels when compared to alternative systems. Additionally, our approach is entirely independent of previous user data, effectively eliminating any potential user bias that could influence the strain analyses.
应变代表了给定区域内局部组织变形的量化。心肌应变已被证明是评估心脏功能的一个非常有用的指标。值得注意的是，与传统的心脏功能指数（如左心室射血分数，LVEF）相比，它在检测微妙的心肌异常方面具有更高的敏感性。然而，由于需要精确跟踪心肌在整个心动周期中的运动，因此应变的估计仍然具有相当大的挑战性。本研究引入了一种新的基于深度学习的流水线，旨在从三维（3D）电影磁共振图像自动且准确地估计心肌应变。因此，我们的研究提出了一种精确量化局部和整体心肌应变的综合流水线。该流水线包括一个监督卷积神经网络（CNN），用于精确分割心肌，以及一个无监督 CNN，用于稳健的左心室运动跟踪，从而能够在人工幻影和真实电影磁共振图像中估计应变。我们的研究对与该领域两个常用商业软件的结果进行了全面比较，包括对内部和用户间变异性的评估。与替代系统相比，所提出的流水线具有明显的可靠性和更低的发散水平。此外，我们的方法完全独立于之前的用户数据，有效地消除了任何可能影响应变分析的潜在用户偏见。
