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

心脏 4D 血流 MRI 中的半自动分割和标志识别

• 批准号: 478365-2014
• 负责人: Samavati, Faramarz
• 金额: $ 1.02万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=619185
• 关键词: Semi; automatic; segmentation; landmark; identification

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.

在这个项目中，我们探索了先进的计算和数学方法，用于分割和识别心脏解剖结构的重要组成部分，并进行流量测量，这对于设计软件工具来处理，分析，可视化和探索4D流MRI数据集是必不可少的。MRI技术提供了一种非侵入性的工具，用于量化和可视化心脏和外周血管的解剖和生理，有助于心脏疾病的诊断。传统上，MRI数据集已经作为表示3D体积的2D图像的堆叠而可用。心脏4D流动MRI是一种相对较新的技术，其提供随时间变化的体积数据集，呈现一系列3D图像。用于获取4D流动MRI数据集的先前技术需要20至40分钟的较长扫描时间，这使得它们在许多情况下令人望而却步。随着新的4D流动MRI数据采集技术的出现，我们的工业合作伙伴可以访问，这些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非常有用的工具数据集，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的死亡原因（世界卫生组织，2013年3月）。心脏解剖结构的复杂性使得自动分割和识别其重要组成部分，如瓣膜，心室心尖等，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必须由熟练的心脏成像仪执行的耗时任务。因此，本项目旨在提供有用的交互式工具和合适的技术来分析和解释这些数据集，这将有助于减少相关放射科医生的工作量。