Temporal diffeomorphic Free Form Deformation (TDFFD) applied to motion and deformation quantification of tagged MRI sequences

International audienceThis paper presents strain quantification results obtained from the Tagged Magnetic Resonance Imaging (TMRI) sequences acquired for the 1 st cardiac Motion Analysis Challenge (cMAC). We applied the Temporal Diffeomorphic Free Form Deformation (TDFFD) algorithm to the phantom and the 15 healthy volunteers of the cMAC database. The TDFFD was modified in two ways. First, we modified the similarity metric to incorporate frame to frame intensity differences. Second, on volunteer sequences, we performed the tracking backward in time since the first frames did not show the contrast between blood and myocardium, making these frames poor choices of reference. On the phantom, we propagated a grid adjusted to tag lines to all frames for visually assessing the influence of the different algorithmic parameters. The weight between the two metric terms appeared to be a critical parameter for making a compromise between good tag tracking while preventing drifts and avoiding tag jumps. For each volunteer, a volumet-ric mesh was defined in the Steady-State Free Precession (SSFP) image, at the closest cardiac time from the last frame of the tagging sequence. Uniform strain patterns were observed over all myocardial segments, as physiologically expected

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

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