Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:545-548. doi: 10.1109/EMBC48229.2022.9871516.
Accurate quantification of myocardium strain in magnetic resonance images is important to correctly diagnose and monitor cardiac diseases. Currently, available methods to estimate motion are based on tracking brightness pattern differences between images. In cine-MR images, the myocardium interior presents an inhered homogeneity, which reduces the accuracy in estimated motion, and consequently strain. Neural networks have recently been shown to be an important tool for a variety of applications, including motion estimation. In this work, we investigate the feasibility of quantifying myocardium strain in cardiac resonance synthetic images using motion generated by a compact and powerful network called Pyramid, Warping, and Cost Volume (PWC). Using the motion generated by the neural network, the radial myocardium strain obtained presents a mean average error of 12.30% +- 6.50%, and in the circumferential direction 1.20% +-0.61 %, better than the two classical methods evaluated. Clinical Relevance- This work demonstrates the feasibility of estimating myocardium strain using motion estimated by a convolutional neural network.
准确量化磁共振图像中的心肌应变对于正确诊断和监测心脏疾病非常重要。目前，用于估计运动的可用方法基于跟踪图像之间亮度模式的差异。在电影磁共振图像中，心肌内部呈现出固有的均匀性，这降低了运动和应变的估计精度。神经网络最近已被证明是一种重要的工具，可用于各种应用，包括运动估计。在这项工作中，我们研究了使用称为 Pyramid、Warping 和 Cost Volume (PWC) 的紧凑而强大的网络生成的运动来量化心脏磁共振合成图像中心肌应变的可行性。使用神经网络生成的运动，径向心肌应变的平均绝对误差为 12.30% +- 6.50%，而在圆周方向为 1.20% +-0.61%，优于评估的两种经典方法。临床相关性- 这项工作证明了使用卷积神经网络估计心肌应变的可行性。
