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

在这个项目中，我们探索了分割和识别心脏解剖的重要组成部分以及执行血流测量所需的高级计算和数学方法，这些方法对于设计处理、分析、可视化和探索4D Flow MRI数据集的软件工具至关重要。MRI技术为量化和可视化心脏和外周血管的解剖和生理提供了一种非侵入性工具，有助于心脏疾病的诊断。传统上，MRI数据集以2D图像堆叠的形式提供，代表3D体积。心脏4D Flow MRI是一种相对较新的技术，它提供随时间变化的体积数据集，呈现一系列3D图像。 以前获取4D Flow MRI数据集的技术需要20到40分钟的较长扫描时间，这在许多情况下使其难以实现。随着新的4D Flow MRI数据采集技术的出现，我们的工业合作伙伴可以访问，此类数据集的扫描时间已减少到约6分钟。这项尖端技术为心脏异常的非侵入性检测提供了极其有用和有用的数据集，增加了心血管疾病的早期诊断机会，心血管疾病是全球主要死亡原因(世界卫生组织，2013年3月)。 心脏解剖结构的复杂性使得心脏瓣膜、心尖部等重要部位的自动分割和识别变得困难和不可靠。另一方面，这种心脏成分的手动分割和识别是一项耗时的任务，必须由熟练的心脏成像员执行。因此，该项目旨在提供有用的交互工具和适当的技术来分析和解释这些数据集，这将有助于减少参与其中的放射科医生的工作量。

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.