Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard for regional myocardium deformation and cardiac strain estimation. However, this technique has not been widely used in clinical diagnosis, as a result of the difficulty of motion tracking encountered with t-MRI images. In this paper, we propose a novel deep learning-based fully unsupervised method for in vivo motion tracking on t-MRI images. We first estimate the motion field (INF) between any two consecutive t-MRI frames by a bi-directional generative diffeomorphic registration neural network. Using this result, we then estimate the Lagrangian motion field between the reference frame and any other frame through a differentiable composition layer. By utilizing temporal information to perform reasonable estimations on spatio-temporal motion fields, this novel method provides a useful solution for motion tracking and image registration in dynamic medical imaging. Our method has been validated on a representative clinical t-MRI dataset; the experimental results show that our method is superior to conventional motion tracking methods in terms of landmark tracking accuracy and inference efficiency.

中文翻译：

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DeepTag：在心脏标记磁共振图像上进行运动跟踪的无监督深度学习方法

心脏标记磁共振成像（t-MRI）是区域心肌变形和心脏应变估计的金标准。然而，由于t-MRI图像遇到运动跟踪的困难，该技术尚未广泛用于临床诊断。在本文中，我们提出了一种新颖的基于深度学习的完全无监督的方法，用于在t-MRI图像上进行体内运动跟踪。我们首先通过双向生成微形配准神经网络来估计任意两个连续的t-MRI帧之间的运动场（INF）。然后，使用此结果，我们通过可微的构图层估计参考帧和任何其他帧之间的拉格朗日运动场。通过利用时间信息对时空运动场进行合理的估算，这种新颖的方法为动态医学成像中的运动跟踪和图像配准提供了有用的解决方案。我们的方法已在代表性的临床t-MRI数据集上得到验证；实验结果表明，该方法在地标跟踪精度和推理效率上均优于传统的运动跟踪方法。