Susana Merino-Caviedes

Groupwise Elastic Registration by a New Sparsity-Promoting Metric: Application to the Alignment of Cardiac Magnetic Resonance Perfusion Images

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.

3D fusion of cine and late-enhanced cardiac magnetic resonance images

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 object-based 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 long-axis 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.

A general interpolation method for symmetric second-rank tensors in two dimensions

A new interpolation method for 2 x 2 symmetric second-rank tensors is proposed. It uses a vector representation of tensors using its eigenvalues and the rotation angle of the major eigenvector with respect to a cartesian coordinate system. These characteristics are then linearly interpolated. Although it is not constricted to positive definite tensors, it preserves this property for tensors with nonnegative eigenvalues. We compare this technique with the matrix coefficient linear in terpolation. The experiments show that our technique improves the results.

An Automated Tensorial Classification Procedure for Left Ventricular Hypertrophic Cardiomyopathy

Cardiovascular diseases are the leading cause of death globally. Therefore, classification tools play a major role in prevention and treatment of these diseases. Statistical learning theory applied to magnetic resonance imaging has led to the diagnosis of a variety of cardiomyopathies states. We propose a two-stage classification scheme capable of distinguishing between heterogeneous groups of hypertrophic cardiomyopathies and healthy patients. A multimodal processing pipeline is employed to estimate robust tensorial descriptors of myocardial mechanical properties for both short-axis and long-axis magnetic resonance tagged images using the least absolute deviation method. A homomorphic filtering procedure is used to align the cine segmentations to the tagged sequence and provides 3D tensor information in meaningful areas. Results have shown that the proposed pipeline provides tensorial measurements on which classifiers for the study of hypertrophic cardiomyopathies can be built with acceptable performance even for reduced samples sets.

Multi-Stencil Streamline Fast Marching: A General 3-D Framework to Determine Myocardial Thickness and Transmurality in Late Enhancement Images

We propose a fully 3-D methodology for the computation of myocardial nonviable 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 nonviable tissue is preserved. The computation is done by means of a partial differential equation framework we have called multi-stencil streamline fast marching. Using it, the myocardial and scarred tissue thickness is simultaneously computed. Experimental results show that the proposed 3-D method allows for the computation of transmurality in myocardial regions where current 2-D methods are not able to as conceived, and it also provides more robust and accurate results in situations where the assumptions on which current 2-D methods are based-i.e., there is a visible endocardial contour and its corresponding epicardial points lie on the same slice-, are not met.

A variationally based weighted re-initialization method for geometric active contours

In geometric active contour algorithms, a re-initialization step must be performed by the level set function to remain close to a signed distance function, in order to avoid numerical instabilities. We propose a new re-initialization method that may be employed as a standalone method to recover the signed distance condition, or may be embedded directly into a variational framework as an additional term for the energy functional. Its purpose is to make the pixels near the propagating contour be less affected by the re-initialization. Experimental results show that whereas previous approaches change the position of the zero level set, our method keeps it virtually unchanged.

Estimation of Healthy and Fibrotic Tissue Distributions in DE-CMR Incorporating CINE-CMR in an EM Algorithm

Delayed Enhancement (DE) Cardiac Magnetic Resonance (CMR) allows practitioners to identify fibrosis in the myocardium. It is of importance in the differential diagnosis and therapy selection in Hypertrophic Cardiomyopathy (HCM). However, most clinical semiautomatic scar quantification methods present high intra- and interobserver variability in the case of HCM. Automatic methods relying on mixture model estimation of the myocardial intensity distribution are also subject to variability due to inaccuracies of the myocardial mask. In this paper, the CINE-CMR image information is incorporated to the estimation of the DE-CMR tissue distributions, without assuming perfect alignment between the two modalities nor the same label partitions in them. For this purpose, we propose an expectation maximization algorithm that estimates the DE-CMR distribution parameters, as well as the conditional probabilities of the DE-CMR labels with respect to the labels of CINE-CMR, with the latter being an input of the algorithm. Our results show that, compared to applying the EM using only the DE-CMR data, the proposed algorithm is more accurate in estimating the myocardial tissue parameters and obtains higher likelihood of the fibrosis voxels, as well as a higher Dice coefficient of the subsequent segmentations.

A variational method for scar segmentation with myocardial contour correction in DE-CMR images

Most automatic scar segmentation methods for cardiac DE-CMR images rely on an existing myocardial segmentation (from CINE-CMR) that is registered to the DE-CMR volume, step where alignment errors are usually introduced. We present a variational method that, with the same inputs, identifies the healthy and scarred tissue and selectively corrects the endocardial and epicardial contours. For this, we tailor an existing multiphase segmentation method to provide different regularization costs for each region, and model the data fidelity energy term with a Bayesian approach that unifies the prior tissue probabilities and the myocardial labels. Experimental results show better overlapping for the ground truth and segmented myocardium, and the segmented scar compares favorably with respect to state of the art methods.

Multiphase level set algorithm for coupled segmentation of multiple regions. Application to MRI segmentation

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.

User Interfaces to Interact with Tensor Fields

Nowadays there is a growing interest in tensor medical imaging modalities. In Diffusion Tensor Magnetic Resonance Imaging (DT-MRI), each pixel is valued with a symmetric second-order tensor describing the spatial properties of diffusion at that point. Therefore, it provides significantly more information than scalar modalities, but this causes the complexity of the interfaces dealing with them to grow. In this chapter, the current situation of user interfaces for tensor fields is reviewed. Tensor user interfaces are difficult to design, given the difficulty of mentally integrating data with so many parameters. This is why a considerable effort must be invested in order to achieve intuitive and easy-to-use interfaces. The display of tensor information plays an important role in this, and we review several existing visualization methods for tensor fields.We must point out that, although most of the applications are graphical interfaces, there are also examples of command-line tools and multimodal interfaces employing virtual environments. We study some of the urrent medical user interfaces for diffusion tensor fields.