Fast strain mapping in abdominal aortic aneurysm wall reveals heterogeneous patterns

Abdominal aortic aneurysm patients are regularly monitored to assess aneurysm development and risk of rupture. A preventive surgical procedure is recommended when the maximum aortic antero-posterior diameter, periodically assessed on two-dimensional abdominal ultrasound scans, reaches 5.5 mm. Although the maximum diameter criterion has limited ability to predict aneurysm rupture, no clinically relevant tool that could complement the current guidelines has emerged so far. In vivo cyclic strains in the aneurysm wall are related to the wall response to blood pressure pulse, and therefore, they can be linked to wall mechanical properties, which in turn contribute to determining the risk of rupture. This work aimed to enable biomechanical estimations in the aneurysm wall by providing a fast and semi-automatic method to post-process dynamic clinical ultrasound sequences and by mapping the cross-sectional strains on the B-mode image. Specifically, the Sparse Demons algorithm was employed to track the wall motion throughout multiple cardiac cycles. Then, the cyclic strains were mapped by means of radial basis function interpolation and differentiation. We applied our method to two-dimensional sequences from eight patients. The automatic part of the analysis took under 1.5 min per cardiac cycle. The tracking method was validated against simulated ultrasound sequences, and a maximum root mean square error of 0.22 mm was found. The strain was calculated both with our method and with the established finite-element method, and a very good agreement was found, with mean differences of one order of magnitude smaller than the image spatial resolution. Most patients exhibited a strain pattern that suggests interaction with the spine. To conclude, our method is a promising tool for investigating abdominal aortic aneurysm wall biomechanics as it can provide a fast and accurate measurement of the cyclic wall strains from clinical ultrasound sequences

Recalage par éléments finis avec partition de l'unité (applications en imagerie médicale)

Cette thèse présente une approche paramétrique pour le recalage d'images. Nous représentons une méthode par éléments finis avec partition de l'unité, dans laquelle le champ de déplacement est représenté comme une fonction localement polynômiale. Les propriétés de partition de l'unité nous permettent de déduire une stratégie d'optimisation efficace en dissociant le problème global en sous-problèmes simples et indépendants de minimisations locales. Nous introduisons ensuite la contrainte de conformité qui force dans une certaine mesure les représentations locales à s'accorder entre elles ; cette contrainte est un moyen de contrôler de manière flexible la globalité du champ de déformation. Dans un premier temps, nous appliquons notre méthode au recalage monomodal d'images CT 3D des poumons pour l'estimation du mouvement respiratoire ; nous la comparons à quatre autres méthodes sur plusieurs critères quantitatifs et qualitatifs. Dans un deuxième temps, nous utilisons notre représentation du champ de déplacement par partitions d'unité pour la segmentation biphasique d'images avec a priori de forme ; le principe est de recaler une forme binaire a priori sur une image afin de la segmenter. La contrainte de conformité est un moyen de forcer la solution à respecter l'a priori de formeIn this work, we present a Partition of Unity Finite Element Method (PUFEM) to compute the transformation between two images, which is represented by a non-rigid, locally polynomial displacement field. The partition of unity property offers an efficient optimization scheme by breaking down the global minimization of the mismatch energy into independent, local minimizations. We then introduce a conformity constraint between the local representations to provide a flexible way to control the globality of the deformation. We first apply our method to register 3D-CT images in order to estimate the respiratory motion; it is compared to four other methods with respect to quantitative and qualitative criteria. Secondly, we use our partition of unity representation for the purpose of two-phase, prior-based image segmentation. The crux is to register a binary prior shape to an image in order to segment it. The conformity constraint compels the solution to be compliant with the shape priorPARIS-DAUPHINE-BU (751162101) / SudocSudocFranceF

O.: Non-rigid image registration using a hierarchical partition of unity finite element method

We use a Hierarchical Partition of Unity Finite Element Method (H-PUFEM) to represent and analyse the non-rigid deformation fields involved in multidimensional image registration. We make use of the Ritz-Galerkin direct variational method to solve non-rigid image registration problems with various deformation constraints. In this method, we directly seek a set of parameters that minimizes the objective function. We thereby avoid the loss of information that may occur when an Euler-Lagrange formulation is used. Experiments are conducted to demonstrate the advantages of our approach when registering synthetic images having little of or no localizing features. As a special case, conformal mapping problems can be accurately solved in this manner. We also illustrate our approach with an application to Cardiac Magnetic Resonance temporal sequences. 1

Real-Time 3D Image Segmentation by User-Constrained Template Deformation

We describe an algorithm for 3D interactive image segmenta- tion by non-rigid implicit template deformation, with two main original features. First, our formulation incorporates user input as inside/outside labeled points to drive the deformation and improve both robustness and accuracy. This yields inequality constraints, solved using an Augmented Lagrangian approach. Secondly, a fast implementation of non-rigid template-to-image registration enables interactions with a real-time visual feedback. We validated this generic technique on 21 Contrast-Enhanced Ultrasound images of kidneys and obtained accurate segmentation results (Dice> 0:93) in less than 3 clicks in average

Robust image registration based on a Partition of Unity Finite Element Method

In this paper, we present a robust, hierarchical partition of unity finite element method (PUFEM) to compute the transformation between two images, which is represented by a non-rigid, locally polynomial displacement field. The partition of unity property offers an efficient optimization scheme by breaking down the global minimization of the mismatch energy into independent, local minimizations. Moreover, the regularization introduced by our approach enables us to control the range of the smoothness. Our method was applied to cardiac ultrasound image sequences to propagate the segmentation of anatomical structures of interest

Prior-based Piecewise-smooth Segmentation by Template Competitive Deformation using Partitions of Unity

Abstract. We propose a new algorithm for two-phase, piecewise-smooth segmentation with shape prior. The image is segmented by a binary template that is deformed by a regular geometric transformation. The choice of the template together with the constraint on the transformation introduce the shape prior. In particular, the topology of the shape is preserved if the transformation is diffeomorphic. The deformation is guided by the maximization of the likelihood of foreground and background intensity models, so that we can refer to this approach as Competitive Deformation. In each region, the intensity is modelled as a smooth approximation of the original image. We represent the transformation using a Partition of Unity Finite Element Method, which consists in representing each component with polynomial approximations within local patches. A conformity constraint between the patches provides a way to control the globality of the deformation. We show several results on synthetic images, as well as on medical data from different modalities.

Generation of Realistic 4D Synthetic CSPAMM Tagged MR Sequences for Benchmarking Cardiac Motion Tracking Algorithms

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Motion estimation in 3D echocardiography using smooth field registration

International audienceThis paper describes an algorithm for motion and deforma- tion quanti cation of 3D cardiac ultrasound sequences. The algorithm is based on the assumption that the deformation eld is smooth inside the myocardium. Thus, we assume that the displacement eld can be represented as the convolution of an unknown eld with a Gaussian kernel. We apply our algorithm to datasets with reliable ground truth: a set of synthetic sequences with known trajectories and a set of sequences of a mechanical phantom implanted with microsonometry crystals. The final publication is available at link.springer.co

Detailed Evaluation of Five 3D Speckle Tracking Algorithms Using Synthetic Echocardiographic Recordings

International audienceA plethora of techniques for cardiac deformation imaging with 3D ultrasound, typically referred to as 3D speckle tracking techniques, are available from academia and industry. Although the benefits of single methods over alternative ones have been reported in separate publications, the intrinsic differences in the data and definitions used makes it hard to compare the relative performance of different solutions. To address this issue, we have recently proposed a framework to simulate realistic 3D echocardiographic recordings and used it to generate a common set of ground-truth data for 3D speckle tracking algorithms, which was made available online. The aim of this study was therefore to use the newly developed database to contrast non-commercial speckle tracking solutions from research groups with leading expertise in the field. The five techniques involved cover the most representative families of existing approaches, namely block-matching, radio-frequency tracking, optical flow and elastic image registration. The techniques were contrasted in terms of tracking and strain accuracy. The feasibility of the obtained strain measurements to diagnose pathology was also tested for ischemia and dyssynchron

Computational and physical phantom setups for the second cardiac motion analysis challenge (cMAC2)

International audienceThis paper describes the data setup of the second cardiac Motion Analysis Challenge (cMac2). The purpose of this challenge is to initiate a public data repository for the benchmark of motion and strain quantification algorithms on 3D ultrasound images. The data currently includes synthetic images that combine ultrasound and biomechanical simulators. We also collected sonomicrometry curves and ultrasound images acquired on a Polyvinyl alcohol phantom