Semi-automatic segmentation and landmark identification in cardiac 4D flow MRI

点击翻译按钮获取中文摘要

In this project, we explore advanced computational and mathematical methods required for segmenting and identifying important components of cardiac anatomy and performing flow measurements, which are essential for designing software tools to process, analyze, visualize, and explore 4D flow MRI datasets. MRI techniques provide a noninvasive tool for quantifying and visualizing the anatomy and physiology of heart and peripheral vessels, helping in the diagnosis of heart diseases. Traditionally, MRI datasets have been made available as stacks of 2D images, representing 3D volumes. Cardiac 4D flow MRI is a relatively new technology that provides time-varying volumetric datasets, presenting a series of 3D images. Prior techniques for acquiring 4D flow MRI datasets required longer scan-times of 20 to 40 minutes, making them prohibitive in many cases. With the advent of new 4D flow MRI data acquisition technology, accessible by our industrial partner, the scan-time for such datasets has been reduced to around six minutes. This cutting edge technology provides extremely useful and instrumental datasets for noninvasive detection of anomalies in the heart, increasing early chances of diagnosis for cardiovascular diseases, which comprise the leading cause of death worldwide (World Health Organization, March 2013). The complexity of cardiac anatomy makes the automatic segmentation and identification of its important components, such as the valves, ventricular apexes, etc., difficult and unreliable. On the other hand, the manual segmentation and identification of such cardiac components is a time-consuming task that must be performed by a skilled cardioimager. Consequently, this project aims to provide useful interactive tools and suitable techniques to analyze and interpret such datasets, which will contribute to reducing the workload of involved radiologists.