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Motion estimation of cardiac MRI videos is crucial for the evaluation ofhuman heart anatomy and function. Recent researches show promising results withdeep learning-based methods. In clinical deployment, however, they sufferdramatic performance drops due to mismatched distributions between training andtesting datasets, commonly encountered in the clinical environment. On theother hand, it is arguably impossible to collect all representative datasetsand to train a universal tracker before deployment. In this context, weproposed a novel fast online adaptive learning (FOAL) framework: an onlinegradient descent based optimizer that is optimized by a meta-learner. Themeta-learner enables the online optimizer to perform a fast and robustadaptation. We evaluated our method through extensive experiments on two publicclinical datasets. The results showed the superior performance of FOAL inaccuracy compared to the offline-trained tracking method. On average, the FOALtook only $0.4$ second per video for online optimization.

FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation