Isabelle E. Magnin

A review of cardiac image registration methods

In this paper, the current status of cardiac image registration methods is reviewed. The combination of information from multiple cardiac image modalities, such as magnetic resonance imaging, computed tomography, positron emission tomography, single-photon emission computed tomography, and ultrasound, is of increasing interest in the medical community for physiologic understanding and diagnostic purposes. Registration of cardiac images is a more complex problem than brain image registration because the heart is a nonrigid moving organ inside a moving body. Moreover, as compared to the registration of brain images, the heart exhibits much fewer accurate anatomical landmarks. In a clinical context, physicians often mentally integrate image information from different modalities. Automatic registration, based on computer programs, might, however, offer better accuracy and repeatability and save time.

Human Atlas of the Cardiac Fiber Architecture: Study on a Healthy Population

Cardiac fibers, as well as their local arrangement in laminar sheets, have a complex spatial variation of their orientation that has an important role in mechanical and electrical cardiac functions. In this paper, a statistical atlas of this cardiac fiber architecture is built for the first time using human datasets. This atlas provides an average description of the human cardiac fiber architecture along with its variability within the population. In this study, the population is composed of ten healthy human hearts whose cardiac fiber architecture is imaged ex vivo with DT-MRI acquisitions. The atlas construction is based on a computational framework that minimizes user interactions and combines most recent advances in image analysis: graph cuts for segmentation, symmetric log-domain diffeomorphic demons for registration, and log-Euclidean metric for diffusion tensor processing and statistical analysis. Results show that the helix angle of the average fiber orientation is highly correlated to the transmural depth and ranges from -41° on the epicardium to +66° on the endocardium. Moreover, we find that the fiber orientation dispersion across the population (13°) is lower than for the laminar sheets (31°). This study, based on human hearts, extends previous studies on other mammals with concurring conclusions and provides a description of the cardiac fiber architecture more specific to human and better suited for clinical applications. Indeed, this statistical atlas can help to improve the computational models used for radio-frequency ablation, cardiac resynchronization therapy, surgical ventricular restoration, or diagnosis and followups of heart diseases due to fiber architecture anomalies.

Texture based medical image indexing and retrieval: application to cardiac imaging

Although digital images indexing and querying techniques have extensively been studied for the last years, few systems are dedicated to medical images today while the need for content-based analysis and retrieval tools increases with the growth of digital medical image databases. We analyze medical image properties and we evaluate Gabor-filter based features extraction for medical images indexing and classification. The goal is to perform clinically relevant queries on large image databases that do not require user supervision. We demonstrate on the concrete case of cardiac imaging that these techniques can be used for indexing, retrieval by similarity queries, and to some extent, extracting clinically relevant information out of the images

Model extraction from magnetic resonance volume data using the deformable pyramid

A general framework for automatic model extraction from magnetic resonance (MR) images is described. The framework is based on a two-stage algorithm. In the first stage, a geometrical and topological multiresolution prior model is constructed. It is based on a pyramid of graphs. In the second stage, a matching algorithm is described. This algorithm is used to deform the prior pyramid in a constrained manner. The topological and the main geometrical properties of the model are preserved, and at the same time, the model adapts itself to the input data. We show that it performs a fast and robust model extraction from image data containing unstructured information and noise. The efficiency of the deformable pyramid is illustrated on a synthetic image. Several examples of the method applied to MR volumes are also represented.

Medical Images Simulation, Storage, and Processing on the European DataGrid Testbed

The European 1ST DataGrid project was a pioneer in identifying the medical imaging field as an application domain that can benefit from Grid technologies. This paper describes how and for which purposes medical imaging applications can be Grid-enabled. Applications that have been deployed on the DataGrid testbed and middleware are described. They relate to medical image manipulation, including image production, secured image storage, and image processing. Results show that Grid technologies are still in their youth to address all issues related to complex medical imaging applications. If the benefit of Grid enabling for some medical applications is clear, there remain opened research and technical issues to develop and integrate all necessary services.

Automated myocardial edge detection from breath-hold cine-MR images: Evaluation of left ventricular volumes and mass☆

This paper describes an automated edge detection method for the delineation of the endo- and epicardial borders of the left ventricle from magnetic resonance (MR) images. The feasibility of this technique was demonstrated by processing temporal series of cardiac MR images obtained in 12 healthy subjects and acquired from the apex to the base of the heart in multiple anatomic short axis planes with a breath-hold cine-MR acquisition sequence. This procedure allows the entire heart to be imaged in less than 5 min. The automatic program correctly identified the edges in most cases. In poor contrasted images, a fast and user-friendly interactive procedure was used to correct the border delineation. The proposed method for the contour tracing requires a limited degree of control by the user and thus considerably reduces the tedious and long operator time inherent in the usual manual contour tracing tool. The left ventricular volumes were directly measured from these sets of contours by using the Simpson rule, allowing the end-diastolic volumes (EDV), the end-systolic volumes (ESV), the ejection fraction (EF) and the myocardial mass to be determined. The values measured in this study with the dedicated software were similar to the literature values (EDV = 78.3 ml/m2; ESV = 21.1 ml/m2; EF = 73%). Associated with the ultrafast breath-hold cine-MR imaging, the described edge detection method provides an efficient clinical tool for the direct assessment of cardiac function.

Mammographic Texture Analysis: An Evaluation Of Risk For Developing Breast Cancer

This paper deals with early and accurate breast cancer risk assessment for women. The use of texture analysis tools for the eventual development of an automatic system is proposed. In a first step, a standard procedure for obtaining x-ray mammograms is set up, the resulting radiographic images then being classified into four risk groups by a specialist. In a second step, specific and selected texture algorithms using both global and local statistical properties of the images are implemented. A number of x-ray mammograms have been studied. One of the resulting important observations is that it seems inappropriate to define a set of distinct classes of risk; rather, an increasing gravity degree correlated to a continuous evolution of the mammographic textures from the lowest to the highest degree of risk is to be preferred. Finally, a systematic comparison between the human classification and the numerical coefficients provided by the texture analysis is performed. The coefficients do not allow risk classification by themselves. A critical examination of these preliminary results leads us to a constructive discussion concerning the future developments of the proposed method.

Two-dimensional spatial and temporal displacement and deformation field fitting from cardiac magnetic resonance tagging

Tagged magnetic resonance imaging is a specially developed technique to noninvasively assess contractile function of the heart. Several methods have been developed to estimate myocardial deformation from tagged image data. Most of these methods do not explicitly impose a continuity constraint through time although myocardial motion is a continuous physical phenomenon. In this paper, we propose to model the spatio-temporal myocardial displacement field by a cosine series model fitted to the entire tagged dataset. The method has been implemented in two dimensions (2D)+time. Its accuracy was successively evaluated on actual tagged data and on a simulated two-dimensional (2D) moving heart model. The simulations show that an overall theoretical mean accuracy of 0.1 mm can be attained with adequate model orders. The influence of the tagging pattern was evaluated and computing time is provided as a function of the model complexity and data size. This method provides an analytical and hierarchical model of the 2D+time deformation inside the myocardium. It was applied to actual tagged data from a healthy subject and from a patient with ischemia. The results demonstrate the adequacy of the proposed model for this evaluation.

Tracking geometrical descriptors on 3-D deformable surfaces: application to the left-ventricular surface of the heart

Motion and deformation analysis of the myocardium are of utmost interest in cardiac imaging. Part of the research is devoted to the estimation of the heart function by analysis of the shape changes of the left-ventricular endocardial surface. However, most clinically used shape-based approaches are often two-dimensional (2-D) and based on the analysis of the shape at only two cardiac instants. Three-dimensional (3-D) approaches generally make restrictive hypothesis about the actual endocardium motion to be able to recover a point-to-point correspondence between two surfaces. The present work is a first step toward the automatic spatio-temporal analysis and recognition of deformable surfaces. A curvature-based and easily interpretable description of the surfaces is derived. Based on this description, shape dynamics is first globally estimated through the temporal shape spectra. Second, a regional curvature-based tracking approach is proposed assuming a smooth deformation. It combines geometrical and spatial information in order to analyze a specific endocardial region. These methods are applied both on true 3-D X-ray data and on simulated normal and abnormal left ventricles. The results are coherent and easily interpretable. Shape dynamics estimations and comparisons between deformable object sequences are now possible through these techniques. This promising framework is a suitable tool for a complete regional description of deformable surfaces.

Multi-object Deformable Templates Dedicated to the Segmentation of Brain Deep Structures

We propose a new way of embedding shape distributions in a topological deformable template. These distributions rely on global shape descriptors corresponding to the 3D moment invariants. In opposition to usual Fourier-like descriptors, they can be updated during deformations at a relatively low cost. The moment-based distributions are included in a framework allowing the management of several simultaneously deforming objects. This framework is dedicated to the segmentation of brain deep nuclei in 3D MR images. The paper focuses on the learning of the shape distributions, on the initialization of the topological model and on the multi-resolution energy minimization process. Results are presented showing the segmentation of twelve brain deep structures.