Cross-subject image registration is the building block for many cardiac studies. In the literature, it is often handled by voxel-wise registration methods. However, studies are lacking to show which methods are more accurate and stable in this context. Aiming at answering this question, this paper evaluates 12 popular registration methods and validates a recently developed method DRAMMS [16] in the context of cross-subject cardiac registration. Our dataset consists of short-axis end-diastole cardiac MR images from 24 subjects, in which non-cardiac structures are removed. Each registration method was applied to all 552 image pairs. Registration accuracy is approximated by Jaccard overlap between deformed expert annotation of source image and the corresponding expert annotation of target image. This accuracy surrogate is further correlated with deformation aggressiveness, which is reflected by minimum, maximum and range of Jacobian determinants. Our study shows that DRAMMS [16] scores high in accuracy and well balances accuracy and aggressiveness in this dataset, followed by ANTs [13], MI-FFD [14], Demons [15], and ART [12]. Our findings in cross-subject cardiac registrations echo those findings in brain image registrations [7].

In this paper, we adapt spectral signatures for capturing morphological changes over time. Advanced techniques for capturing temporal shape changes frequently rely on first registering the sequence of shapes and then analyzing the corresponding set of high dimensional deformation maps. Instead, we propose a simple encoding motivated by the observation that small shape deformations lead to minor refinements in the spectral signature composed of the eigenvalues of the Laplace operator. The proposed encoding does not require registration, since spectral signatures are invariant to pose changes. We apply our representation to the shapes of the ventricles extracted from 22 cine MR scans of healthy controls and Tetralogy of Fallot patients. We then measure the accuracy score of our encoding by training a linear classifier, which outperforms the same classifier based on volumetric measurements.