Automatic classification of left ventricular regional wall motion abnormalities in echocardiography images using nonrigid image registration

Identification and classification of left ventricular (LV) regional wall motion (RWM) abnormalities on echocardiograms has fundamental clinical importance for various cardiovascular disease assessments especially in ischemia. In clinical practice, this evaluation is still performed visually which is highly dependent on training and experience of the echocardiographers and therefore suffers from significant interobserver and intraobserver variability. This paper presents a new automatic technique, based on nonrigid image registration for classifying the RWM of LV in a three-point scale. In this algorithm, we register all images of one cycle of heart to a reference image (end-diastolic image) using a hierarchical parametric model. This model is based on an affine transformation for modeling the global LV motion and a B-spline free-form deformation transformation for modeling the local LV deformation. We consider image registration as a multiresolution optimization problem. Finally, a new regional quantitative index based on resultant parameters of the hierarchical transformation model is proposed for classifying RWM in a three-point scale. The results obtained by our method are quantitatively evaluated to those obtained by two experienced echocardiographers visually as gold standard on ten healthy volunteers and 14 patients (two apical views) and resulted in an absolute agreement of 83 and a relative agreement of 99 . Therefore, this diagnostic system can be used as a useful tool as well as reference visual assessment to classify RWM abnormalities in clinical evaluation. © 2013 Society for Imaging Informatics in Medicine

Personalised Finite-Element Models using Image Registration in Parametric Space

Heart failure (HF) is a chronic clinical condition in which the heart fails to pump enough blood to meet the metabolic needs of the body. Patients have reduced physical performance and can see their quality of life severely impaired; around 40-70% of patients diagnosed of HF die within the first year following diagnosis. It is underestimated that 900,000 people in the UK currently suffer from HF. HF has a big impact on the NHS, representing 1 million inpatient bed, 5% of all emergency medical admission to hospitals and costs 2% of the total NHS budget. The annual incidence of new diagnoses is reported as 93,000 people in England alone – and this figure is already increasing at a rate above that at which population is ageing [1]. Cardiac resynchronisation therapy (CRT) has become established as an effective solution to treat selected patients with HF. The research presented in this thesis has been conducted as part of a large EPSRC-Funded project on the theme of Grand Challenges in Heathcare, with co-investigators from King’s College London (KCL), Imperial College London, University College London (UCL) and the University of Sheffield. The aim is to develop and to apply modelling techniques to simulate ventricular mechanics and CRT therapy in patient cohorts from Guy’s Hospital (London) and from the Sheffield Teaching Hospitals Trust. This will lead to improved understanding of cardiac physiological behaviour and how diseases affect normal cardiac performance, and to improved therapy planning by allowing candidate interventions to be simulated before they are applied on patients. The clinical workflow within the hospital manages the patient through the processes of diagnosis, therapy planning and follow-up. The first part of this thesis focuses on the development of a formal process for the integration of a computational analysis workflow, including medical imaging, segmentation, model construction, model execution and analysis, into the clinical workflow. During the early stages of the project, as the analysis workflow was being compiled, a major bottle-neck was identified regarding the time required to build accurate, patient-specific geometrical meshes from the segmented images. The second part of this thesis focuses on the development of a novel approach based on the use of image registration to improve the process of construction of a high-quality personalised finite element mesh for an individual patient. Chapter 1 summarises the clinical context and introduces the tools and processes that are applied in this thesis. Chapter 2 describes the challenges and the implementation of a computational analysis workflow and its integration into a clinical environment. Chapter 3 describes the theoretical underpinnings of the image registration algorithm that has been developed to address the problem of construction of high-quality personalised meshes. The approach includes the use of regularisation terms that are designed to improve the mesh quality. The selection and implementation of the regularisation terms is discussed in detail in Chapter 4. Chapter 5 describes the application of the method to a series of test problems, whilst Chapter 6 describes the application to the patient cohort in the clinical study. Chapter 7 demonstrates that the method, developed for robust mesh construction, can readily be applied to determine boundary conditions for computational fluid dynamics (CFD) analysis. Chapter 8 provides a summary of the achievements of the thesis, together with suggestions for further work