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.

本文提出了一种基于深度学习的全自动运动跟踪方法，可以对心脏标记核磁共振成像（t-MRI）图像中的运动进行跟踪，并且在动态医学成像方面提供了有用的解决方案。实验证明，与传统运动跟踪方法相比，这种新方法在地标跟踪的准确性和推理效率方面都表现出了优越性。