Definition of a 4D continuous polar transformation for the tracking and the analysis of LV motion

International audienceA 4D polar transformation is defined to describe the left ventricle (LV) motion and a method is presented to estimate it from sequences of 3D images. The transformation is defined in 3D-planispheric coordinates by a small number of parameters involved in a set of simple linear equations. It is continuous and regular in time and space, periodicity in time can be imposed. The local motion can be easily decomposed into a few canonical motions (centripetal contraction, rotation around the long-axis, elevation). To recover the motion from original data, the 4D polar transformation is calculated using an adaptation of the Iterative Closest Point algorithm. We present the mathematical framework and a demonstration of its feasability on a gated SPECT sequence

Definition of a 4D Continuous Polar Transformation for the Tracking and the Analysis of LV Motion

Cardiologists assume that analysis of the motion of the heart (especially the left ventricle) can give precise information about the health of the myocardium. A 4D polar transformation is defined to describe the left ventricle (LV) motion and a method is presented to estimate it from sequences of 3D images. The transformation is defined in 3D-planispheric coordinates by a small number of parameters involved in a set of simple linear equations. It is continuous and regular in time and space, periodicity in time can be imposed. The local motion can be easily decomposed into a few canonical motions (centripetal contraction, rotation around the long-axis, elevation). To recover the motion from original data, the 4D polar transformation is calculated using an adaptation of the Iterative Closest Point algorithm. We present the mathematical framework and a demonstration of its feasability on a set of synthetic but realistic datapoints, simulating the motion of the LV and on a gated SPECT sequence

Definition of a four-dimensional continuous planispheric transformation for the tracking and the analysis of left-ventricle motion

International audienceCardiologists assume that analysis of the motion of the heart (especially the left ventricle) can provide useful information about the health of the myocardium. A 4-D polar transformation is defined to describe the left-ventricle (LV) motion and a method is presented to estimate it from sequences of 3-D images. The transformation is defined in 3-D planispheric coordinates (3PC) by a small number of parameters involved in a set of simple linear equations. It is continuous and regular in time and space, and periodicity in time can be imposed. The local motion can be easily decomposed into a few canonical motions (radial motion, rotation around the long-axis, elevation). To recover the motion from original data, the 4-D polar transformation is calculated using an adaptation of the iterative closest-point algorithm. We present the mathematical framework and a demonstration of its feasability on a series of gated SPECT sequences

Analysis of cardiac motion using MRI and nonrigid image registration

Imperial Users onl

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

Doctor of Philosophy

dissertationThe statistical study of anatomy is one of the primary focuses of medical image analysis. It is well-established that the appropriate mathematical settings for such analyses are Riemannian manifolds and Lie group actions. Statistically defined atlases, in which a mean anatomical image is computed from a collection of static three-dimensional (3D) scans, have become commonplace. Within the past few decades, these efforts, which constitute the field of computational anatomy, have seen great success in enabling quantitative analysis. However, most of the analysis within computational anatomy has focused on collections of static images in population studies. The recent emergence of large-scale longitudinal imaging studies and four-dimensional (4D) imaging technology presents new opportunities for studying dynamic anatomical processes such as motion, growth, and degeneration. In order to make use of this new data, it is imperative that computational anatomy be extended with methods for the statistical analysis of longitudinal and dynamic medical imaging. In this dissertation, the deformable template framework is used for the development of 4D statistical shape analysis, with applications in motion analysis for individualized medicine and the study of growth and disease progression. A new method for estimating organ motion directly from raw imaging data is introduced and tested extensively. Polynomial regression, the staple of curve regression in Euclidean spaces, is extended to the setting of Riemannian manifolds. This polynomial regression framework enables rigorous statistical analysis of longitudinal imaging data. Finally, a new diffeomorphic model of irrotational shape change is presented. This new model presents striking practical advantages over standard diffeomorphic methods, while the study of this new space promises to illuminate aspects of the structure of the diffeomorphism group

Cardiac motion and deformation estimation in tagged magnetic resonance imaging

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Electrónica Médica)Cardiovascular diseases are the main cause of death in Europe, with an estimate of 4.3 million deaths each year. The assessment of the regional wall deformation is a relevant clinical indicator, and can be used to detect several cardiac lesions. Nowadays, this study can be performed using several image modalities. In the current thesis, we focus on tagged Magnetic Resonance imaging (t-MRI) technique. Such technique allows acquiring images with tags on the myocardium, which deform with the muscle. The present thesis intends to assess the left ventricle (LV) deformation using radial and circumferential strain. To compute such strain values, both endo- and epicardial contours of the LV are required. As such, a new framework to automatically assess the LV function is proposed. This framework presents: (i) an automatic segmentation technique, based on a tag suppression strategy followed by an active contour segmentation method, and (ii) a tracking approach to extract myocardial deformation, based on a non-rigid registration method. The automatic segmentation uses the B-spline Explicit Active Surface framework, which was previously applied in ultra-sound and cine-MRI images. In both cases, a real-time and accurate contour was achieved. Regarding the registration step, starting from a state-of-art approach, termed sequential 2D, we suggest a new method (termed sequential 2D+t), where the temporal information is included on the model. The tracking methods were first tested on synthetic data to study the registration parameters influence. Furthermore, the proposed and original methods were applied on porcine data with myocardial ischemia. Both methods were able to detect dysfunctional regions. A comparison between the strain curve in the sequential 2D and sequential 2D+t strategies was also shown. As conclusion, a smoothing effect in the strain curve was detected in the sequential 2D+t strategy. The validation of the segmentation approach uses a human dataset. A comparison between the manual contour and the proposed segmentation method results was performed. The results, suggest that proposed method has an acceptable performance, removing the tedious task related with manual segmentation and the intra-observer variability. Finally, a comparison between the proposed framework and the currently available commercial software was performed. The commercial software results were obtained from core-lab analysis. An acceptable result (r = 0.601) was achieved when comparing the strain peak values. Importantly, the proposed framework appears to present a more acceptable result.As doenças cardiovasculares são a principal causa de morte na Europa, com aproximadamente 4.7 milhões de mortes por ano. A avaliação da deformação do miocárdio a um nível local é um importante indicador clínico e pode ser usado para a deteção de lesões cardíacas. Este estudo é normalmente realizado usando várias modalidades de imagem médica. Nesta tese, a Resonância Magnética (RM) marcada foi a técnica selecionada. Estas imagens têm marcadores no músculo cardíaco, os quais se deformam com o miocárdio e podem ser usados para o estudo da deformação cardíaca. Nesta tese, pretende-se estudar a deformação radial e circunferencial do ventrículo esquerdo (VE). Assim, um contorno do endo- e epicárdio no VE é essencial. Desta forma, uma ferramenta para o estudo da deformação do VE foi desenvolvida. Esta possui: (i) um método de segmentação automático, usando uma estratégia de supressão dos marcadores, seguido de uma segmentação c um contorno ativo, e (ii) um método de tracking para determinação da deformação cardíaca, baseado em registo não rígido. A segmentação automática utiliza a ferramenta B-spline Explicit Active Surface, que foi previamente aplicada em imagens de ultrassons e cine-RM. Em ambos os casos, uma segmentação em tempo real e com elevada exatidão foi alcançada. Vários esquemas de registo foram apresentados. Neste ponto, começando com uma técnica do estado da arte (designada de sequencial 2D), uma nova metodologia foi proposta (sequencial 2D+t), onde a informação temporal é incorporada no modelo. De forma a analisar a influência dos parâmetros do registo, estes foram estudados num dataset sintético. De seguida, os diferentes esquemas de registo foram testados num dataset suíno com isquemia. Ambos os métodos foram capazes de detetar as regiões disfuncionais. De igual forma, utilizando as curvas de deformação obtidas para cada um dos métodos propostos, foi possível observar uma suavização na direção temporal para o método sequencial 2D+t. Relativamente à segmentação, esta foi validada com um dataset humano. Um contorno manual foi comparado com o obtido pelo método proposto. Os resultados sugerem que a nova estratégia é aceitável, sendo mais rápida do que a realização de um contorno manual e eliminando a variabilidade entre observadores. Por fim, realizou-se uma comparação entre a ferramenta proposta e um software comercial (com análise de core-lab). A comparação entre os valores de pico da deformação exibe uma correlação plausível (r=0.601). Contudo, é importante notar, que a nova ferramenta tende a apresentar um resultado mais aceitável

Foetal echocardiographic segmentation

Congenital heart disease affects just under one percentage of all live births [1]. Those defects that manifest themselves as changes to the cardiac chamber volumes are the motivation for the research presented in this thesis. Blood volume measurements in vivo require delineation of the cardiac chambers and manual tracing of foetal cardiac chambers is very time consuming and operator dependent. This thesis presents a multi region based level set snake deformable model applied in both 2D and 3D which can automatically adapt to some extent towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts. The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD). The level set methods presented in this thesis have an optional shape prior term for constraining the segmentation by a template registered to the image in the presence of shadowing and heavy noise. When applied to real data in the absence of the template the MSSCD algorithm is initialised from seed primitives placed at the centre of each cardiac chamber. The voxel statistics inside the chamber is determined before evolution. The MSSCD stops at open boundaries between two chambers as the two approaching level set fronts meet. This has significance when determining volumes for all cardiac compartments since cardiac indices assume that each chamber is treated in isolation. Comparison of the segmentation results from the implemented snakes including a previous level set method in the foetal cardiac literature show that in both 2D and 3D on both real and synthetic data, the MSSCD formulation is better suited to these types of data. All the algorithms tested in this thesis are within 2mm error to manually traced segmentation of the foetal cardiac datasets. This corresponds to less than 10% of the length of a foetal heart. In addition to comparison with manual tracings all the amorphous deformable model segmentations in this thesis are validated using a physical phantom. The volume estimation of the phantom by the MSSCD segmentation is to within 13% of the physically determined volume

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

Automated Segmentation of Left and Right Ventricles in MRI and Classification of the Myocarfium Abnormalities

A fundamental step in diagnosis of cardiovascular diseases, automated left and right ventricle (LV and RV) segmentation in cardiac magnetic resonance images (MRI) is still acknowledged to be a difficult problem. Although algorithms for LV segmentation do exist, they require either extensive training or intensive user inputs. RV segmentation in MRI has yet to be solved and is still acknowledged a completely unsolved problem because its shape is not symmetric and circular, its deformations are complex and varies extensively over the cardiac phases, and it includes papillary muscles. In this thesis, I investigate fast detection of the LV endo- and epi-cardium surfaces (3D) and contours (2D) in cardiac MRI via convex relaxation and distribution matching. A rapid 3D segmentation of the RV in cardiac MRI via distribution matching constraints on segment shape and appearance is also investigated. These algorithms only require a single subject for training and a very simple user input, which amounts to one click. The solution is sought following the optimization of functionals containing probability product kernel constraints on the distributions of intensity and geometric features. The formulations lead to challenging optimization problems, which are not directly amenable to convex-optimization techniques. For each functional, the problem is split into a sequence of sub-problems, each of which can be solved exactly and globally via a convex relaxation and the augmented Lagrangian method. Finally, an information-theoretic based artificial neural network (ANN) is proposed for normal/abnormal LV myocardium motion classification. Using the LV segmentation results, the LV cavity points is estimated via a Kalman filter and a recursive dynamic Bayesian filter. However, due to the similarities between the statistical information of normal and abnormal points, differentiating between distributions of abnormal and normal points is a challenging problem. The problem was investigated with a global measure based on the Shannon\u27s differential entropy (SDE) and further examined with two other information-theoretic criteria, one based on Renyi entropy and the other on Fisher information. Unlike the existing information-theoretic studies, the approach addresses explicitly the overlap between the distributions of normal and abnormal cases, thereby yielding a competitive performance. I further propose an algorithm based on a supervised 3-layer ANN to differentiate between the distributions farther. The ANN is trained and tested by five different information measures of radial distance and velocity for points on endocardial boundary