Generation of Myocardial Wall Surface Meshes from Segmented MRI

This paper presents a novel method for the generation of myocardial wall surface meshes from segmented 3D MR images, which typically have strongly anisotropic voxels. The method maps a premeshed sphere to the surface of the segmented object. The mapping is defined by the gradient field of the solution of the Laplace equation between the sphere and the surface of the object. The same algorithm is independently used to generate the surface meshes of the epicardium and endocardium of the four cardiac chambers. The generated meshes are smooth despite the strong voxel anisotropy, which is not the case for the marching cubes and related methods. While the proposed method generates more regular mesh triangles than the marching cubes and allows for a complete control of the number of triangles, the generated meshes are still close to the ones obtained by the marching cubes. The method was tested on 3D short-axis cardiac MR images with strongly anisotropic voxels in the long-axis direction. For the five tested subjects, the average in-slice distance between the meshes generated by the proposed method and by the marching cubes was 0.4 mm

Modelos Deformáveis em Imagem Médica

Modelos deformáveis são actualmente bastante utilizados em imagem médica pois, através da utilização de princípios físicos, simulam de forma bastante satisfatória o comportamento dos objectos reais.Basicamente os modelos deformáveis são inicializados junto dos objectos a considerar, por processos automáticos ou semi-automáticos, e a aproximação para a posição final desejada é conseguida através de um processo de minimização de energia. Esta minimização de energia é verificada quando o modelo atinge o equilíbrio, entre as suas forças internas e as forças externas originadas pelos dados e por eventuais forças impostas pelo utilizador.Neste relatório são apresentados os fundamentos dos modelos deformáveis e indicados alguns exemplos de aplicação em imagem médica, nomeadamente na segmentação, no emparelhamento, no alinhamento e na reconstrução de dados 2D e 3D.Palavras-chave: Contornos activos, imagem médica, modelos deformáveis.Deformable models are currently very used in medical image since, through the use of physical principles, they simulate quite satisfactory the real objects behavior.Basically the deformable models are placed in the image near to the objects to be considered, by automatic or semi-automatic processes, and the approach to the desired final position is obtained through an energy minimization process. This energy minimization is verified when the model reaches the equilibrium, between its internal forces and the external forces originated by the data and eventual forces imposed by the user.In this report are presented the deformable models fundaments and indicated some application examples in medical imaging field, namely in segmentation, matching, alignment and in the reconstruction of 2D and 3D data.Keywords: Active contours, deformable models, medical image

Ultrasonido tridimensional en cardiología

El análisis de imágenes cardiovasculares constituyeuna herramienta útil para el diagnóstico,tratamiento y monitoreo de enfermedades cardiovasculares.Las técnicas de procesamiento de imágenespermiten la evaluación cuantitativa no-invasiva de la funcióncardiaca, proporcionando información morfológica, funcionaly dinámica. Los progresos tecnológicos recientes en ultrasonidohan permitido incrementar la calidad del tratamientoal paciente, gracias al uso de técnicas modernas de procesamientoy análisis de las imágenes. Sin embargo, la adquisiciónde estas imágenes tridimensionales (3D) dinámicas conduce ala producción de grandes volúmenes de datos para procesar,a partir de los cuales las estructuras cardiacas deben ser extraídasy analizadas durante el ciclo cardiaco. Herramientas deextracción, de visualización tridimensional, y de cuantificaciónson usadas actualmente dentro de la rutina clínica, pero desafortunadamentenecesitan de una interacción importante conel médico. Estos elementos justifican el desarrollo de nuevosalgoritmos eficaces y robustos para la extracción de estructurasy estimación del movimiento cardiaco a partir de imágenestridimensionales. Como resultado, poner a disposicióndel personal clínico nuevos medios para evaluar de maneraprecisa la anatomía y la función cardiaca a partir de imágenestridimensionales, representa un avance certero dentro de lainvestigación de una descripción completa del corazón a partirde un ´único examen. El objetivo de este artículo es mostrarcuáles han sido los avances en imagenología cardiaca 3D porultrasonido y adicionalmente observar qué áreas han sido estudiadasbajo esta modalidad imagenológica

The mitral valve computational anatomy and geometry analysis

We present a novel methodology to characterize and quantify the Mitral Valve (MV) geometry and physical attributes in a multi-resolution framework. A multi-scale decomposition was implemented to model the MV geometry by using superquadric shape primitives and spectral reconstruction of the finer-scale geometric details. Superquadrics provide a basis to normalize the size and approximate a basic model of the MV geometry. The point-wise difference between the original geometry and the superquadric model denotes the finer-scale geometric details, which can be modeled as a scalar attribute for the MV model development. The additive decomposition of the basic MV geometry from geometric details (attributes) allows recovering the actual geometry by superposition of the superquadric approximation and the finer-details model. We implemented a lasso optimization algorithm to perform spectral analysis and develop the Fourier reconstruction of the geometric details. The spectral modeling enabled us to resample the geometric details or use spectral filters in order to adjust the spatial resolution in the model reconstruction. It also provides the basis to control the level of detail in the final model reconstruction by applying low-pass filters in the frequency domain. The higher-order attributes such as internal fiber architecture can be integrated with the geometric models using the same framework. We applied our pipeline to create models of three ovine MVs based on computed-tomography 3D images with micrometer resolution. We were able to quantify the MV leaflet geometry, reconstruct models with custom level of geometric details, and develop medial representation of the MV leaflet structure. The results show that our methodology for geometry analysis provides a basis for assessing patient-specific geometries and facilitates developing population-averaged models. Ultimately, this approach allows building personalized image-based computational models for medical device design and surgical treatment simulations.Mechanical Engineerin

Tracking and motion analysis of the left ventricle with deformable superquadrics

We present a new approach to analyse the deformation of the left ventricle of the heart based on a parametric model that gives a compact representation of a set of points in a 3-D image. We present a strategy for tracking surfaces in a sequence of 3-D cardiac images. Following tracking, we then infer quantitative parameters which characterize: left ventricle motion, volume of left ventricle, ejection fraction, amplitude and twist component of cardiac motion. We explain the computation of these parameters using our model. Experimental results are shown in time sequences of two modalities of medical images, nuclear medicine and X-ray computed tomography (CT). Video sequences presenting these results are on the CD-ROM

Cardiac image computing for myocardial infarction patients

Cardiovascular diseases (CVDs), which are a prime cause of global mortality, are disorders that affect the heart and blood vessels' functioning. CVDs may cause consequent complications, due to occlusion in a blood vessel and present as impaired cardiac wall functioning (myocardium). Identifying such impairment (infarction) of the myocardium is of great clinical interest, as it can reveal the nature of altered cardiac topography (ventricular remodelling) to aid the associated intervention decisions. With recent advances in cardiac imaging, such as Magnetic Resonance (MR) imaging, the visualisation and identification of infarcted myocardium has been routinely and effectively used in clinical practice. Diagnosing infarcted myocardium is achieved clinically through the late gadolinium enhancement (LGE) test, which acquires MR images after injecting a gadolinium-based contrast agent (GBCA). Due to the increased accuracy and reproducibility, LGE has emerged as the gold-standard MR imaging test in identifying myocardial infarction. However, clinical studies have reported gadolinium deposition concerns in different body organs and adverse outcomes in patients with advanced kidney failure, over time. Such incidents have motivated researchers to look into the development of both accurate as well as safe diagnostic tools. Emerging research on identifying infarcted myocardium utilises myocardial strain to safely identify infarcted myocardium, which has been addressed in the presented study. For example, myocardial strain represents the shortening or lengthening of the myocardium. If the myocardium is infarcted, then the corresponding strain values differ compared to the healthy myocardium. This finding can be identified and utilised for clinical applications. The research presented in this thesis aims to identify infarcted myocardium accurately and safely by using myocardial strain (shortening or lengthening of the myocardium). To achieve the aforementioned aim, the research methodology is divided into six objectives. The initial objectives relate to the development of a novel myocardial tracking method. The middle objectives relate to the development of clinical application methods, and the final objectives concern the validation of the developed methods through clinical studies and associated datasets. The research presented in this thesis has addressed the following research question: Research question 1: How can a 2D myocardial tracking and strain calculation method be developed using the 2D local weighted mean function and structural deformation within the myocardium? Research question 2: How can a 3D myocardial tracking and strain calculation method be developed using the 3D local weighted mean function to calculate 3D myocardial strain? Research question 3: How can 2D circumferential strain of the myocardium be used in identifying infarcted left ventricular segments for the diagnosis of myocardial infarction patients? In literature, myocardial tracking and strain calculation methods have limited extension to 3D and dependency on tissue material properties. Moreover, additional limitations, such as limited inclusion of structural deformation details within the myocardium, are found in the literature. Therefore, methods are likely to become subjective or numerically unstable during computation. Moreover, the inclusion of myocardial details with grid-tagging MRI, for structural deformation within the myocardium, is more realistic compared to cine MRI.   The aforementioned limitations are overcome by proposing a novel Hierarchical Template Matching method, which performs non-rigid image registration among grid-tagging MR images of a cardiac cycle. This is achieved by employing a local weighted mean transformation function. The proposed non-rigid image registration method does not require the use of tissue material properties. Grid-tagging MRI is used to capture wall function within the myocardium, and the local weighted mean function is used for numerical stability. The performance of the developed methods is evaluated with multiple error measures and with a benchmark framework. This benchmark framework has provided an open-access 3D dataset, a set of validation methods, and results of four leading methods for comparison. Validation methods include qualitative and quantitative methods. The qualitative assessment of outcomes and verified ground truth for the quantitative evaluation of results are followed from the benchmark framework paper (Tobon-Gomez, Craene, Mcleod, et al., 2013). 2D HTM method has reported the root mean square error of point tracking in left ventricular slices, which are the basal slice 0.31±0.07 mm, the upper mid-ventricular slice 0.37±0.06 mm, the mid-ventricular slice 0.41±0.05 mm, and the apical slice 0.32±0.08 mm. The mid-ventricular slice has a significantly higher 4% (P=0.05) mean root mean square error compared to the other slices. However, the other slices do not have a significant difference among them. Compared to the benchmark free form deformation method, HTM has a mean error of 0.35±0.05 mm, which is 17% (P=0.07, CI:[-0.01,0.35]) reduced to the free form deformation method. Our technical method has shown the 3D extension of HTM and a method without using material properties, which is advantageous compared to the methods which are limited to 2D or dependent on material properties. Moreover, the 3D HTM has demonstrated the use of 3D local weighted mean function in 3D myocardial tracking. While comparing to the benchmark methods, it was found that the median tracking error of 3D HTM is comparable to benchmark methods and has very few outliers compared to them. The clinical results are validated with LGE imaging. The quantitative error measure is the area under the curve (AUC) of sensitivity vs 1-specificity curve of the receiver operating characteristic (ROC) test. The achieved AUC value in detecting infarcted segments in basal, mid-ventricular, and apical slices are 0.85, 0.82, and 0.87, respectively. Calculating AUC with 95% confidence level, the confidence intervals of lower and upper mean AUC values in basal, mid-ventricular and apical slices are [0.80, 0.89], [0.74, 0.85], and [0.78, 0.91], respectively. Overall, considering the detections of LGE imaging as the base, our method has an accuracy of AUC 0.73 (P=0.05) in identifying infarcted left ventricular segments. The developed methods have shown, systematically, a promising approach in identifying infarcted left ventricular segments by image processing method and without using GBCA-based LGE imaging.