Computing thickness of irregularly-shaped thin walls using a locally semi-implicit scheme with extrapolation to solve the Laplace equation: Application to the right ventricle.

Cardiac Magnetic Resonance (CMR) Imaging is currently considered the gold standard imaging modality in cardiology. However, it is accompanied by a tradeoff between spatial resolution and acquisition time. Providing accurate measures of thin walls relative to the image resolution may prove challenging. One such anatomical structure is the cardiac right ventricle. Methods for measuring thickness of wall-like anatomical structures often rely on the Laplace equation to provide point-to-point correspondences between both boundaries. This work presents limex, a novel method to solve the Laplace equation using ghost nodes and providing extrapolated values, which is tested on three different datasets: a mathematical phantom, a set of biventricular segmentations from CMR images of ten pigs and the database used at the RV Segmentation Challenge held at MICCAI'12. Thickness measurements using the proposed methodology are more accurate than state-of-the-art methods, especially with the coarsest image resolutions, yielding mean L1 norms of the error between 43.28% and 86.52% lower than the second-best methods on the different test datasets. It is also computationally affordable. Limex has outperformed other state-of-the-art methods in classifying RV myocardial segments by their thickness.

Time-efficient three-dimensional transmural scar assessment provides relevant substrate characterization for ventricular tachycardia features and long-term recurrences in ischemic cardiomyopathy.

Delayed gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) imaging requires novel and time-efficient approaches to characterize the myocardial substrate associated with ventricular arrhythmia in patients with ischemic cardiomyopathy. Using a translational approach in pigs and patients with established myocardial infarction, we tested and validated a novel 3D methodology to assess ventricular scar using custom transmural criteria and a semiautomatic approach to obtain transmural scar maps in ventricular models reconstructed from both 3D-acquired and 3D-upsampled-2D-acquired LGE-CMR images. The results showed that 3D-upsampled models from 2D LGE-CMR images provided a time-efficient alternative to 3D-acquired sequences to assess the myocardial substrate associated with ischemic cardiomyopathy. Scar assessment from 2D-LGE-CMR sequences using 3D-upsampled models was superior to conventional 2D assessment to identify scar sizes associated with the cycle length of spontaneous ventricular tachycardia episodes and long-term ventricular tachycardia recurrences after catheter ablation. This novel methodology may represent an efficient approach in clinical practice after manual or automatic segmentation of myocardial borders in a small number of conventional 2D LGE-CMR slices and automatic scar detection.

A Second Order Multi-Stencil Fast Marching Method with a Non-Constant Local Cost Model.

The Fast Marching method is widely employed in several fields of image processing. Some years ago a Multi-Stencil version (MSFM) was introduced to improve its accuracy by solving the equation for a set of stencils and choosing the best solution at each considered node. The following work proposes a modified numerical scheme for MSFM to take into account the variation of the local cost, which has proven to be second order. The influence of the stencil set choice on the algorithm outcome with respect to stencil orthogonality and axis swapping is also explored, where stencils are taken from neighborhoods of varying radius. The experimental results show that the proposed schemes improve the accuracy of their original counterparts, and that the use of permutation-invariant stencil sets provides robustness against shifted vector coordinates in the stencil set.

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.

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.

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.