This paper proposes a methodology for the joint alignment of a sequence of images based on a groupwise registration procedure by using a new family of metrics that exploit the expected sparseness of the temporal intensity curves corresponding to the aligned points. Therefore, this methodology is able to tackle the alignment of temporal sequences of images in which the represented phenomenon varies in time. Specifically, we have applied it to the correction of motion in contrast-enhanced first-pass perfusion cardiac magnetic resonance images. The time sequence is elastically registered as a whole by using the aforementioned family of multi-image metrics and jointly optimizing the parameters of the transformations involved. The proposed metrics are able to cope with dynamic changes in the intensity content of corresponding points in the sequence guided by the assumption that these changes allow for a sparse representation in a properly selected frame. Results have shown the statistically significant improvement in the performance of the proposed metric with respect to previous groupwise registration metrics for the problem at hand, which is especially relevant to correct for elastic deformations.

We propose a fully three-dimensional methodology for the computation of myocardial non-viable tissue transmurality in contrast enhanced magnetic resonance images. The outcome is a continuous map defined within the myocardium where not only current state-of-the-art measures of transmurality can be calculated, but also information on the location of non-viable tissue is preserved. The computation is done by means of a partial differential equation framework we have called Multi-Stencil Streamline Fast Marching (MSSFM). Using it, the myocardial and scarred tissue thickness is simultaneously computed. Experimental results show that the proposed 3D method allows for the computation of transmurality in myocardial regions where current 2D methods are not able to as conceived, and it also provides more robust and accurate results in situations where the assumptions on which current 2D methods are based -i.e., there is a visible endocardial contour and its corresponding epicardial points lie on the same slice-, are not met.

Classic geometric active contour algorithms have the limitation of segmenting the image into only two regions: background and object of interest. A new multiphase level set algorithm for the segmentation of two or more regions of interest is proposed. This algorithm avoids by construction the presence of overlapped and void regions and no additional coupling terms are required. In addition, the number of iterations needed to reach convergence is small. The algorithm has been tested against a state-of-the-art multiphase method on both simulated and real Magnetic Resonance Imaging (MRI) volumes with favorable results.

A procedure to fuse the information of short-axis cine and late enhanced magnetic resonance images is presented. First a coherent 3D reconstruction of the images is obtained by objectbased interpolation of the information of contiguous slices in stacked short-axis cine acquisitions and by the correction of slice misalignments with the aid of a set of reference longaxis slices. Then, late enhanced stacked images are also interpolated and aligned with the anatomical information. Thus, the complementary information provided by both modalities is combined in a common frame of reference and in a nearly isotropic grid, which is not possible with existing fusion procedures. Numerical improvement is established by comparing the distances between unaligned and aligned manual segmentations of the myocardium in both modalities. Finally, a set of snapshots illustrate the improvement in the information overlap and the ability to reconstruct the gradient in the long-axis.

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