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Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard forregional myocardium deformation and cardiac strain estimation. However, thistechnique has not been widely used in clinical diagnosis, as a result of thedifficulty of motion tracking encountered with t-MRI images. In this paper, wepropose a novel deep learning-based fully unsupervised method for in vivomotion tracking on t-MRI images. We first estimate the motion field (INF)between any two consecutive t-MRI frames by a bi-directional generativediffeomorphic registration neural network. Using this result, we then estimatethe Lagrangian motion field between the reference frame and any other framethrough a differentiable composition layer. By utilizing temporal informationto perform reasonable estimations on spatio-temporal motion fields, this novelmethod provides a useful solution for motion tracking and image registration indynamic medical imaging. Our method has been validated on a representativeclinical t-MRI dataset; the experimental results show that our method issuperior to conventional motion tracking methods in terms of landmark trackingaccuracy and inference efficiency.

DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images