Segmentation of Myocardial Boundaries in Tagged Cardiac MRI Using Active Contours: A Gradient-Based Approach Integrating Texture Analysis

The noninvasive assessment of cardiac function is of first importance for the diagnosis of cardiovascular diseases. Among all medical scanners only a few enables radiologists to evaluate the local cardiac motion. Tagged cardiac MRI is one of them. This protocol generates on Short-Axis (SA) sequences a dark grid which is deformed in accordance with the cardiac motion. Tracking the grid allows specialists a local estimation of cardiac geometrical parameters within myocardium. The work described in this paper aims to automate the myocardial contours detection in order to optimize the detection and the tracking of the grid of tags within myocardium. The method we have developed for endocardial and epicardial contours detection is based on the use of texture analysis and active contours models. Texture analysis allows us to define energy maps more efficient than those usually used in active contours methods where attractor is often based on gradient and which were useless in our case of study, for quality of tagged cardiac MRI is very poor

Analysis of cardiac motion using MRI and nonrigid image registration

Imperial Users onl

Motion tracking tMRI datasets to quantify abnormal left ventricle motion using finite element modelling

According to `The Atlas of Heart Disease and Stroke'[MMMG04] published by the World Health Organization, heart disease accounts for nearly half the deaths in both the developed and developing countries and is the world's single biggest killer. However, early detection of a diseased heart condition can prevent many of these fatalities. Regional wall motion abnormalities of the heart precede both ECG abnormalities and chest pain as an indicator of myocardial ischaemia and are an excellent indicator of coronary stenosis [GZM97]. These motion abnormalities of the heart muscle are difficult to observe and track, because the heart is a relatively smooth organ with few landmarks and non-rigid motion with a twisting motion or tangential component. The MRI tissue-tagging technique gives researchers the first glimpse into how the heart actually beats. This research uses the tagged MRI images of the heart to create a three dimensional model of a beating heart indicating the stress of a region. Tagged MRI techniques are still developing and vary vastly, meaning that there needs to be a methodology that can adapt to these changes rapidly and effectively, to meet the needs of the evolving technology. The focus of this research is to develop and test such a methodology by the means of a Strain Estimation Pipeline along with an effective way of validating any changes made to the individual processes that it comprises of

MR imaging of left-ventricular function : novel image acquisition and analysis techniques.

Many cardiac diseases, such as myocardial ischemia, secondary to coronary artery disease, may be identified and localized through the analysis of cardiac deformations. Early efforts for quantifying ventricular wall motion used surgical implantation and tracking of radiopaque markers with X-ray imaging in canine hearts [1]. Such techniques are invasive and affect the regional motion pattern of the ventricular wall during the marker tracking process and, clearly are not feasible clinically. Noninvasive imaging techniques are vital and have been widely applied to the clinic. MRI is a noninvasive imaging technique with the capability to monitor and assess the progression of cardiovascular diseases (CVD) so that effective procedures for the care and treatment of patients can be developed by physicians and researchers. It is capable of providing 3D analysis of global and regional cardiac function with great accuracy and reproducibility. In the past few years, numerous efforts have been devoted to cardiac motion recovery and deformation analysis from MR imaging sequences. In order to assess cardiac function, there are two categories of indices that are used: global and regional indices. Global indices include ejection fraction, cavity volume, and myocardial mass [2]. They are important indices for cardiac disease diagnosis. However, these global indices are not specific for regional analysis. A quantitative assessment of regional parameters may prove beneficial for the diagnosis of disease and evaluation of severity and the quantification of treatment [3]. Local measures, such as wall deformation and strain in all regions of the heart, can provide objective regional quantification of ventricular wall function and relate to the location and extent of ischemic injury. This dissertation is concerned with the development of novel MR imaging techniques and image postprocessing algorithms to analyze left ventricular deformations. A novel pulse sequence, termed Orthogonal CSPAMM (OCSPAMM), has been proposed which results in the same acquisition time as SPAMM for 2D deformation estimation while keeping the main advantages of CSPAMM [4,5]: i.e., maintaining tag contrast through-out the ECG cycle. Different from CSPAMM, in OCSPAMM the second tagging pulse orientation is rotated 90 degrees relative to the first one so that motion information can be obtained simultaneously in two directions. This reduces the acquisition time by a factor of two as compared to the traditional CSPAMM, in which two separate imaging sequences are applied per acquisition. With the application of OCSPAMM, the effect of tag fading encountered in SPAMM tagging due to Tl relaxation is mitigated and tag deformations can be visualized for the entire cardiac cycle, including diastolic phases. A multilevel B-spline fitting method (MBS) has been proposed which incorporates phase-based displacement information for accurate calculation of 2D motion and strain from tagged MRI [6, 7]. The proposed method combines the advantages of continuity and smoothness of MBS, and makes use of phase information derived from tagged MR images. Compared to previous 2D B-spline-based deformation analysis methods, MBS has the following advantages: 1) It can simultaneously achieve a smooth deformation while accurately approximating the given data set; 2) Computationally, it is very fast; and 3) It can produce more accurate deformation results. Since the tag intersections (intersections between two tag lines) can be extracted accurately and are more or less distributed evenly over the myocardium, MBS has proven effective for 2D cardiac motion tracking. To derive phase-based displacements, 2D HARP and SinMod analysis techniques [8,9] were employed. By producing virtual tags from HARP /SinMod and calculating intersections of virtual tag lines, more data points are obtained. In the reference frame, virtual tag lines are the isoparametric curves of an undeformed 2D B-spline model. In subsequent frames, the locations of intersections of virtual tag lines over the myocardium are updated with phase-based displacement. The advantage of the technique is that in acquiring denser myocardial displacements, it uses both real and virtual tag line intersections. It is fast and more accurate than 2D HARP and SinMod tracking. A novel 3D sine wave modeling (3D SinMod) approach for automatic analysis of 3D cardiac deformations has been proposed [10]. An accelerated 3D complementary spatial modulation of magnetization (CSPAMM) tagging technique [11] was used to acquire complete 3D+t tagged MR data sets of the whole heart (3 dynamic CSPAMM tagged MRI volume with tags in different orientations), in-vivo, in 54 heart beats and within 3 breath-holds. In 3D SinMod, the intensity distribution around each pixel is modeled as a cosine wave front. The principle behind 3D SinMod tracking is that both phase and frequency for each voxel are determined directly from the frequency analysis and the displacement is calculated from the quotient of phase difference and local frequency. The deformation fields clearly demonstrate longitudinal shortening during systole. The contraction of the LV base towards the apex as well as the torsional motion between basal and apical slices is clearly observable from the displacements. 3D SinMod can automatically process the image data to derive measures of motion, deformations, and strains between consecutive pair of tagged volumes in 17 seconds. Therefore, comprehensive 4D imaging and postprocessing for determination of ventricular function is now possible in under 10 minutes. For validation of 3D SinMod, 7 3D+t CSPAMM data sets of healthy subjects have been processed. Comparison of mid-wall contour deformations and circumferential shortening results by 3D SinMod showed good agreement with those by 3D HARP. Tag lines tracked by the proposed technique were also compared with manually delineated ones. The average errors calculated for the systolic phase of the cardiac cycles were in the sub-pixel range

Détection robuste et automatique des contours myocardiques sur des séquences IRM cardiaques marquées

L'évaluation non invasive de la fonction cardiaque présente un intérêt majeur pour le diagnostic et le suivi de pathologies cardio-vasculaires. L'IRM cardiaque marquée (ou taggée) permet de mesurer des paramètres anatomiques et fonctionnels du myocarde. Ce protocole fait apparaître de manière non invasive une grille sur la zone ventriculaire gauche se déformant avec le myocarde. Le suivi de cette grille permet ainsi d'estimer le déplacement intra-myocardique. L'objectif de notre étude est d'automatiser la détection et le suivi des contours endocardique et épicardique du ventricule gauche afin d'optimiser l'étude quantitative 2D+T de la contraction pariétale. La méthode que nous avons développée est fondée sur l'utilisation de l'analyse de texture associée à un modèle de contour actif. En effet, l'analyse de texture permet de générer de meilleures cartes de potentiels que les méthodes classiques (telles que le calcul du gradient par exemple) inefficaces compte-tenu de la faible qualité des images. Cette approche permet l'obtention de résultats satisfaisants à la fois en terme de précision et de reproductibilité

Reconstruction and analysis of 4D heart motion from tagged MR images.

Luo Guo.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 97-109).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.2Chapter 1.2 --- Basics --- p.3Chapter 1.2.1 --- Anatomy of Human Heart --- p.3Chapter 1.2.2 --- The Philosophy of MRI --- p.5Chapter 1.2.3 --- MRI in Practice --- p.7Chapter 1.3 --- Cardiac MR Images Analysis --- p.7Chapter 1.3.1 --- Heart Boundary Segmentation --- p.7Chapter 1.3.2 --- Motion Reconstruction --- p.13Chapter 1.4 --- Summary and Thesis Overview --- p.17Chapter 2 --- Tracking Tags in SPAMM Images --- p.21Chapter 2.1 --- Introduction --- p.21Chapter 2.2 --- The Snake Model --- p.28Chapter 2.3 --- The Improved Snake Model: Tracking Tags Using Snakes --- p.30Chapter 2.3.1 --- Imaging Protocol --- p.30Chapter 2.3.2 --- Model Formulation --- p.31Chapter 2.3.3 --- Numerical Solution --- p.39Chapter 2.4 --- Experimental Results --- p.44Chapter 3 --- B-Spline Based LV Motion Reconstruction --- p.52Chapter 3.1 --- Introduction --- p.52Chapter 3.2 --- LV Shape: Generalized Deformable Ellipsoid --- p.56Chapter 3.3 --- The New Geometric Model: Generalized Prolate Spheroid --- p.58Chapter 3.3.1 --- Generalized Prolate Spheroid --- p.58Chapter 3.3.2 --- Initial Geometric Fitting --- p.59Chapter 3.4 --- Fast Motion Reconstruction: The Enhanced Hi- erarchical Motion Decomposition --- p.65Chapter 3.4.1 --- Hierarchical Motion Decomposition --- p.65Chapter 3.4.2 --- Motion Reconstruction --- p.68Chapter 3.4.3 --- Implementation --- p.76Chapter 3.4.4 --- Time Smoothing --- p.77Chapter 3.5 --- Experimental Results --- p.79Chapter 3.5.1 --- Geometric Fitting --- p.79Chapter 3.5.2 --- Motion Reconstruction --- p.79Chapter 4 --- Conclusion --- p.93Bibliography --- p.10

Multiscale combination of physically-based registration and deformation modeling

Abstract 1 In this paper we present a novel multiscale approach to recovery of nonrigid motion from sequences of registered intensity and range images. The main idea o f our approach is that a nite element (FEM) model can naturally handle both registration and deformation modeling using a single model-driving strategy. The method includes a multiscale iterative algorithm based on analysis of the undirected Hausdor distance to recover corresp ondences. The method is evaluated with resp ect to speed, accur acy, and noise sensitivity. A dvantages of the pr oposed a p p r oach ar e demonstr ated using man-made elastic materials and human skin motion. Experiments with regular grid featur esare used for performance comparison with a conventional approach (separate snakes and FEM models). It is shown that the new method does not requir ea grid and can adapt the model to available object featur es

Medical Image Analysis: Progress over two decades and the challenges ahead

International audienceThe analysis of medical images has been woven into the fabric of the pattern analysis and machine intelligence (PAMI) community since the earliest days of these Transactions. Initially, the efforts in this area were seen as applying pattern analysis and computer vision techniques to another interesting dataset. However, over the last two to three decades, the unique nature of the problems presented within this area of study have led to the development of a new discipline in its own right. Examples of these include: the types of image information that are acquired, the fully three-dimensional image data, the nonrigid nature of object motion and deformation, and the statistical variation of both the underlying normal and abnormal ground truth. In this paper, we look at progress in the field over the last 20 years and suggest some of the challenges that remain for the years to come