Global and regional left ventricular myocardial deformation measures by magnetic resonance feature tracking in healthy volunteers: comparison with tagging and relevance of gender

This work was funded by a grant from the Engineering and Physical Sciences Research Council (EP/G030693/1) and supported by the Oxford British Heart Foundation Centre of Research Excellence and the National Institute for Health Research Oxford Biomedical Research Centr

On motion in dynamic magnetic resonance imaging: Applications in cardiac function and abdominal diffusion

La imagen por resonancia magnética (MRI), hoy en día, representa una potente herramienta para el diagnóstico clínico debido a su flexibilidad y sensibilidad a un amplio rango de propiedades del tejido. Sus principales ventajas son su sobresaliente versatilidad y su capacidad para proporcionar alto contraste entre tejidos blandos. Gracias a esa versatilidad, la MRI se puede emplear para observar diferentes fenómenos físicos dentro del cuerpo humano combinando distintos tipos de pulsos dentro de la secuencia. Esto ha permitido crear distintas modalidades con múltiples aplicaciones tanto biológicas como clínicas. La adquisición de MR es, sin embargo, un proceso lento, lo que conlleva una solución de compromiso entre resolución y tiempo de adquisición (Lima da Cruz, 2016; Royuela-del Val, 2017). Debido a esto, la presencia de movimiento fisiológico durante la adquisición puede conllevar una grave degradación de la calidad de imagen, así como un incremento del tiempo de adquisición, aumentando así tambien la incomodidad del paciente. Esta limitación práctica representa un gran obstáculo para la viabilidad clínica de la MRI. En esta Tesis Doctoral se abordan dos problemas de interés en el campo de la MRI en los que el movimiento fisiológico tiene un papel protagonista. Éstos son, por un lado, la estimación robusta de parámetros de rotación y esfuerzo miocárdico a partir de imágenes de MR-Tagging dinámica para el diagnóstico y clasificación de cardiomiopatías y, por otro, la reconstrucción de mapas del coeficiente de difusión aparente (ADC) a alta resolución y con alta relación señal a ruido (SNR) a partir de adquisiciones de imagen ponderada en difusión (DWI) multiparamétrica en el hígado.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

Fully automatic left ventricular myocardial strain estimation in 2D short-axis tagged magnetic resonance imaging

Cardiovascular diseases are among the leading causes of death and frequently result in local myocardial dysfunction. Among the numerous imaging modalities available to detect these dysfunctional regions, cardiac deformation imaging through tagged magnetic resonance imaging (t-MRI) has been an attractive approach. Nevertheless, fully automatic analysis of these data sets is still challenging. In this work, we present a fully automatic framework to estimate left ventricular myocardial deformation from t-MRI. This strategy performs automatic myocardial segmentation based on B-spline explicit active surfaces, which are initialized using an annular model. A non-rigid image-registration technique is then used to assess myocardial deformation. Three experiments were set up to validate the proposed framework using a clinical database of 75 patients. First, automatic segmentation accuracy was evaluated by comparing against manual delineations at one specific cardiac phase. The proposed solution showed an average perpendicular distance error of 2.35 +/- 1.21 mm and 2.27 +/- 1.02 mm for the endo- and epicardium, respectively. Second, starting from either manual or automatic segmentation, myocardial tracking was performed and the resulting strain curves were compared. It is shown that the automatic segmentation adds negligible differences during the strain-estimation stage, corroborating its accuracy. Finally, segmental strain was compared with scar tissue extent determined by delay-enhanced MRI. The results proved that both strain components were able to distinguish between normal and infarct regions. Overall, the proposed framework was shown to be accurate, robust, and attractive for clinical practice, as it overcomes several limitations of a manual analysis.FCT—Fundacão para a Ciência e a Tecnologia, Portugal, and the European Social Found, European Union, for funding support through the Programa Operacional Capital Humano (POCH) in the scope of the PhD grants SFRH/BD/95438/2013 (P Morais) and SFRH/BD/93443/2013 (S Queirós). This work was supported by the projects NORTE-07-0124-FEDER-000017 and NORTE-01-0145-FEDER-000013, co-funded by Programa Operacional Regional do Norte, Quadro de Referência Estratégico Nacional, through Fundo Europeu de Desenvolvimento Regional (FEDER). The authors would also like to acknowledge the EU (FP7) framework program, for the financial support of the DOPPLER-CIP project (grant no. 223615)info:eu-repo/semantics/publishedVersio

Myocardial tissue tagging with cardiovascular magnetic resonance

Cardiovascular magnetic resonance (CMR) is currently the gold standard for assessing both global and regional myocardial function. New tools for quantifying regional function have been recently developed to characterize early myocardial dysfunction in order to improve the identification and management of individuals at risk for heart failure. Of particular interest is CMR myocardial tagging, a non-invasive technique for assessing regional function that provides a detailed and comprehensive examination of intra-myocardial motion and deformation. Given the current advances in gradient technology, image reconstruction techniques, and data analysis algorithms, CMR myocardial tagging has become the reference modality for evaluating multidimensional strain evolution in the human heart. This review presents an in depth discussion on the current clinical applications of CMR myocardial tagging and the increasingly important role of this technique for assessing subclinical myocardial dysfunction in the setting of a wide variety of myocardial disease processes

Comparative Study on Medical Image Classification Techniques

This brief study compares the proposed RGSA algorithm with other recent methods by several experiments to indicate that proposed 3DGLCM and SGLDM with SVM classifier is more efficient and accurate. The accuracy results of this study imply how well their experimental results were found to give more accurate results of classifying tumors. The center of interest for this study was made on supervised classification approaches on 2D MRI images of brain tumors. This paper gives the comparative study of various approaches that was used to identify the tumor cells with classifiers

Intensity Based Non-rigid Registration of 3D Whole Mouse Optical and MR Image Volumes

Novel magnetic resonance (MR) imaging techniques can be validated using accurate co-registration with histology. Whole-animal histological sections allow for simultaneous analysis of multiple tissues, and may also aid in registration by providing contextual information and structural support to tissues which if isolated from the body would be difficult to register. This thesis explores the feasibility of co-registration between whole mouse histology with 3D MR images using an intermediate optical image volume acquired during tissue sectioning. Of the two transformations required for this approach, 3D co-registration of MR and optical images is more challenging to perform due to changes in contrast, slice orientation, and resolution between these modalities. Here, an automated non-rigid registration technique utilizing mutual information is proposed to accurately register 3D whole mouse optical and MR images as a first step towards automated registration of histology. Validation of this technique was accomplished through calculation of post-registration target registration error

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