4D Multi object segmentation based on MR image sequences - Medical application for evaluation of myocardial differences in shape and function after infarction

基于 MR 图像序列的 4D 多对象分割 - 评估梗塞后心肌形状和功能差异的医学应用

基本信息

批准号：
263745607
负责人：
Dr. Jan Ehrhardt
金额：
--
依托单位：
Institut für Medizinische Biometrie und Epidemiologie
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2014
资助国家：
德国
起止时间：
2013-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/263745607?language=en
关键词：
4D Multi object segmentation based

项目摘要

Myocardial infarction (MI) is the leading cause of death for both men and women worldwide in the western civilization. The quality of life and the course of disease for MI patients depend on the revitalization of the myocardium and avoiding the development of a persistent dysfunctional contraction of the heart, which can lead to progressive impairment of the heart function combined with cardiac remodeling. Early detection of patients with risk of remodeling is clinical relevant because effective therapies have to be initiated early to avoid remodeling.The aim of this work is to develop new automatic methods for detection, quantification and prediction of myocardial remodeling based on spatio temporal MRI datasets. Therefore, a new workflow has to be developed that enables automatic pre-processing of the datasets, a model based segmentation and motion field estimation in 4D MRI as well as quantitative parameter extraction, analysis and visualization. For development and evaluation a comprehensive data pool of baseline and follow-up MRI dataset of MI patients exists. All datasets were acquired with standard imaging parameters. MRI data of healthy subjects with high temporal and/or spatial resolution exists as well. Overall more than 360 anonymized MRI datasets are available for this project. They contain multiple MRI sequences (e.g. Cine-MRI, LGE-MRI, T2w-MRI) and manual segmentations of the relevant cardiac structures.A central aspect of this work is the development of a model based approach. This method uses a new integrated segmentation and motion estimation of the left and right ventricle approach and considers knowledge about shape and shape variations as well as the typical motion of the heart. Another aspect is the quantitative analysis and classification of clinical MRI datasets. Here, learning based classification algorithms are used. Therefore, relevant parameters characterizing shape and motion of the heart are extracted and analyzed providing an automatic detection and prognosis of myocardial remodeling.

心肌梗塞（MI）是全球在西方文明中男女死亡的主要原因。 MI患者的生活质量和疾病进程取决于心肌的振兴以及避免心脏持续性功能障碍收缩的发展，这可能导致心脏功能逐渐损害，并结合心脏重塑。早期发现重塑风险的患者与临床相关，因为必须尽早开始进行有效的疗法以避免重塑。这项工作的目的是开发基于空间时间MRI数据集的新自动方法来检测，定量和预测心肌重塑。因此，必须开发一个新的工作流程，以实现数据集的自动预处理，4D MRI中的基于模型的分割和运动场估计以及定量参数提取，分析和可视化。为了开发和评估，存在基线的全面数据库和MI患者的随访MRI数据集。所有数据集都使用标准成像参数获取。具有高时间和/或空间分辨率的健康受试者的MRI数据也存在。总体而言，该项目可用于360多个匿名MRI数据集。它们包含多个MRI序列（例如Cine-MRI，LGE-MRI，T2W-MRI）和相关心脏结构的手动分割。这项工作的中心方面是开发基于模型的方法。该方法使用左心室和右心室方法的新的集成分割和运动估计，并考虑了有关形状和形状变化以及心脏典型运动的知识。另一个方面是临床MRI数据集的定量分析和分类。在这里，使用基于学习的分类算法。因此，提取并分析了表征心脏形状和运动的相关参数，提供了心肌重塑的自动检测和预后。