  Motion estimation of cardiac MRI videos is crucial for the evaluation of
human heart anatomy and function. Recent researches show promising results with
deep learning-based methods. In clinical deployment, however, they suffer
dramatic performance drops due to mismatched distributions between training and
testing datasets, commonly encountered in the clinical environment. On the
other hand, it is arguably impossible to collect all representative datasets
and to train a universal tracker before deployment. In this context, we
proposed a novel fast online adaptive learning (FOAL) framework: an online
gradient descent based optimizer that is optimized by a meta-learner. The
meta-learner enables the online optimizer to perform a fast and robust
adaptation. We evaluated our method through extensive experiments on two public
clinical datasets. The results showed the superior performance of FOAL in
accuracy compared to the offline-trained tracking method. On average, the FOAL
took only $0.4$ second per video for online optimization.
