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Objective: Cardiovascular magnetic resonance-feature tracking (CMR-FT)represents a group of methods for myocardial strain estimation from cardiaccine MRI images. Established CMR-FT methods are mainly based on optical flow orpairwise registration. However, these methods suffer from either inaccurateestimation of large motion or drift effect caused by accumulative trackingerrors. In this work, we propose a deformable groupwise registration methodusing a locally low-rank (LLR) dissimilarity metric for CMR-FT. Methods: Theproposed method (Groupwise-LLR) tracks the feature points by a groupwiseregistration-based two-step strategy. Unlike the globally low-rank (GLR)dissimilarity metric, the proposed LLR metric imposes low-rankness on localimage patches rather than the whole image. We quantitatively comparedGroupwise-LLR with the Farneback optical flow, a pairwise registration method,and a GLR-based groupwise registration method on simulated and in vivodatasets. Results: Results from the simulated dataset showed that Groupwise-LLRachieved more accurate tracking and strain estimation compared with the othermethods. Results from the in vivo dataset showed that Groupwise-LLR achievedmore accurate tracking and elimination of the drift effect in late-diastole.Inter-observer reproducibility of strain estimates was similar between allstudied methods. Conclusion: The proposed method estimates myocardial strainsmore accurately due to the application of a groupwise registration-basedtracking strategy and an LLR-based dissimilarity metric. Significance: Theproposed CMR-FT method may facilitate more accurate estimation of myocardialstrains, especially in diastole, for clinical assessments of cardiacdysfunction.

Deformable Groupwise Registration Using a Locally Low-Rank Dissimilarity Metric for Myocardial Strain Estimation from Cardiac Cine MRI Images