Construction of a Statistical Atlas of the Whole Heart from a Large 4D CT Database

International audienceWe present in this work an efficient and robust framework for the construction of a high-resolution and spatio-temporal atlas of the whole heart from a database of 138 CT 4D images, the largest sample to be used for cardiac statistical modeling to date. The data is drawn from a variety of pathologies, which benefits its generalization to new subjects and physiological studies. In the proposed technique, spatial and temporal normalization based on non-rigid image registration are used to synthesize a population mean image from all CT image. With the resulting transformation, a detailed 3D mesh representation of the atlas is warped to fit all images in each subject and phase. The obtained level of anatomical detail (a total of 13 cardiac structures) and the extendability of the atlas present an advantage over most existing cardiac models published previously

3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation

We present a novel automated method to segment the myocardium of both left and right ventricles in MRI volumes. The segmentation is consistent in 3D across the slices such that it can be directly used for mesh generation. Two specific neural networks with multi-scale coarse-to-fine prediction structure are proposed to cope with the small training dataset and trained using an original loss function. The former segments a slice in the middle of the volume. Then the latter iteratively propagates the slice segmentations towards the base and the apex, in a spatially consistent way. We perform 5-fold cross-validation on the 15 cases from STACOM to validate the method. For training, we use real cases and their synthetic variants generated by combining motion simulation and image synthesis. Accurate and consistent testing results are obtained

Constrained manifold learning for the characterization of pathological deviations from normality

International audienceThis paper describes a technique to (1) learn the representation of a pathological motion pattern from a given population, and (2) compare individuals to this population. Our hypothesis is that this pattern can be modeled as a deviation from normal motion by means of non-linear embedding techniques. Each subject is represented by a 2D map of local motion abnormalities, obtained from a statistical atlas of myocardial motion built from a healthy population. The algorithm estimates a manifold from a set of patients with varying degrees of the same disease, and compares individuals to the training population using a mapping to the manifold and a distance to normality along the manifold. The approach extends recent manifold learning techniques by constraining the manifold to pass by a physiologically meaningful origin representing a normal motion pattern. Interpolation techniques using locally adjustable kernel improve the accuracy of the method. The technique is applied in the context of cardiac resynchronization therapy (CRT), focusing on a specific motion pattern of intra-ventricular dyssynchrony called septal flash (SF). We estimate the manifold from 50 CRT candidates with SF and test it on 37 CRT candidates and 21 healthy volunteers. Experiments highlight the relevance of nonlinear techniques to model a pathological pattern from the training set and compare new individuals to this pattern

Characterizing Pathological Deviations from Normality using Constrained Manifold-Learning

International audienceWe propose a technique to represent a pathological pattern as a deviation from normality along a manifold structure. Each subject is represented by a map of local motion abnormalities, obtained from a statistical atlas of motion built from a healthy population. The algorithm learns a manifold from a set of patients with varying degrees of the same pathology. The approach extends recent manifold-learning techniques by constraining the manifold to pass by a physiologically meaningful origin representing a normal motion pattern. Individuals are compared to the manifold population through a distance that combines a mapping to the manifold and the path along the manifold to reach its origin. The method is applied in the context of cardiac resynchronization therapy (CRT), focusing on a specific motion pattern of intra-ventricular dyssyn-chrony called septal flash (SF). We estimate the manifold from 50 CRT candidates with SF and test it on 38 CRT candidates and 21 healthy volunteers. Experiments highlight the need of nonlinear techniques to learn the studied data, and the relevance of the computed distance for comparing individuals to a specific pathological pattern

Optimization of beam parameters and iodine quantification in dual-energy contrast enhanced digital breast tomosynthesis

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Image-based estimation of myocardial acceleration using TDFFD: a phantom study

International audienceIn this paper, we propose to estimate myocardial acceleration using a temporal di↵eomorphic free-form deformation (TDFFD) algorithm. The use of TDFFD has the advantage of providing B-spline parameterized velocities, thus temporally smooth, which is an asset for the computation of acceleration. The method is tested on 3D+t echocar-diographic sequences from a realistic physical heart phantom, in which ground truth displacement is known in some regions. Peak endocardial acceleration (PEA) error was 20.4%, the main hypothesis for error being the low temporal resolution of the sequences. The allure of the acceleration profile was reasonably preserved. Our method suggests a non-invasive technique to measure cardiac acceleration that may be used to improve the monitoring of cardiac mechanics and consecutive therapy planning

Temporal diffeomorphic Free Form Deformation to quantify changes induced by left and right bundle branch block and pacing

International audienceThis paper presents motion and deformation quantification results obtained from synthetic and in vitro phantom data provided by the second cardiac Motion Analysis Challenge at STACOM-MICCAI. We applied the Temporal Diffeomorphic Free Form Deformation (TDFFD) algorithm to the datasets. This algorithm builds upon a diffeomorphic version of the FFD, to provide a 3D + t continuous and differentiable transform. The similarity metric includes a comparison between consecutive images, and between a reference and each of the following images. Motion and strain accuracy were evaluated on synthetic 3D ultrasound sequences with known ground truth motion. Experiments were also conducted on in vitro acquisitions

Volume overload impact on 3D right ventricular shape and strain: comparative analysis of tetralogy of Fallot and atrial septal defect patients

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Segmentation and registration coupling from short-axis Cine MRI: application to infarct diagnosis

In pressInternational audienceEstimating regional deformation of the myocardium from Cine MRI has the potential to locate abnormal tissue. Regional deformation of the left ventricle is commonly estimated using either segmentation or 3D+t registration. Segmentation is often performed at each instant separately from the others. It can be tedious and does not guarantee temporal causality. On the other hand, extracting regional parameters through image registration is highly dependent on the initial segmenta-tion chosen to propagate the deformation fields and may not be consistent with the myocardial contours. In this paper, we propose an intermediate approach that couples segmentation and registration in order to improve temporal causality while removing the influence of the chosen initial segmentation. We propose to apply the deformation fields from image registration (sparse Bayesian registration) to every segmentation of the cardiac cycle and combine them for more robust regional measurements. As an illustration, we describe local deformation through the measurement of AHA regional volumes. Maximum regional volume change is extracted and compared across scar and non-scar regions defined from delayed enhancement MRI on 20 ST-elevation myocardial infarction patients. The proposed approach shows (i) more robustness in extracting regional volumes than direct segmentation or standard registration and (ii) better performance in detecting scar