  Objective: Cardiovascular magnetic resonance-feature tracking (CMR-FT)
represents a group of methods for myocardial strain estimation from cardiac
cine MRI images. Established CMR-FT methods are mainly based on optical flow or
pairwise registration. However, these methods suffer from either inaccurate
estimation of large motion or drift effect caused by accumulative tracking
errors. In this work, we propose a deformable groupwise registration method
using a locally low-rank (LLR) dissimilarity metric for CMR-FT. Methods: The
proposed method (Groupwise-LLR) tracks the feature points by a groupwise
registration-based two-step strategy. Unlike the globally low-rank (GLR)
dissimilarity metric, the proposed LLR metric imposes low-rankness on local
image patches rather than the whole image. We quantitatively compared
Groupwise-LLR with the Farneback optical flow, a pairwise registration method,
and a GLR-based groupwise registration method on simulated and in vivo
datasets. Results: Results from the simulated dataset showed that Groupwise-LLR
achieved more accurate tracking and strain estimation compared with the other
methods. Results from the in vivo dataset showed that Groupwise-LLR achieved
more accurate tracking and elimination of the drift effect in late-diastole.
Inter-observer reproducibility of strain estimates was similar between all
studied methods. Conclusion: The proposed method estimates myocardial strains
more accurately due to the application of a groupwise registration-based
tracking strategy and an LLR-based dissimilarity metric. Significance: The
proposed CMR-FT method may facilitate more accurate estimation of myocardial
strains, especially in diastole, for clinical assessments of cardiac
dysfunction.

Image and Video Processing;Computer Vision and Pattern Recognition