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

Ipe, Susan

Abstract

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.

Citation

Ipe, S. (2015). Motion tracking tMRI datasets to quantify abnormal left ventricle motion using finite element modelling. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4220477

• Thesis Type: Thesis
• Deposit Date: Jul 27, 2018
• Publicly Available Date: Feb 23, 2023
• Keywords: Computer science
• Public URL: https://hull-repository.worktribe.com/output/4220477
• Additional Information: Department of Computer Science, The University of Hull
• Award Date: Jan 1, 2015