A Multi-Disciplinary Approach to Solve the Inverse Problem of Electro- and Magnetocardiography

解决心电图和心磁图反问题的多学科方法

• 批准号: 191020717
• 负责人: Professor Dr. Olaf Dössel, Ph.D.
• 金额: --
• 依托单位: School of Mathematics and Statistics
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2011
• 资助国家: 德国
• 起止时间: 2010-12-31 至 2014-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/191020717?language=en
• 关键词: Multi; Disciplinary; Approach; Solve; Inverse

In electrocardiography (ECG), the heart is studied by interpreting voltages measured on the body surface. The spatial accuracy of the ECG can be improved by adding more sensors and by modeling the signals with the help of physics and mathematics. In the inverse problem of ECG, the aim is to estimate the sources of the cardiac electric sources or the electric potential on the surface of the heart from the measured signal at the body surface. This problem is illposed: simplifying assumptions, a-priori information, and regularization are needed.The aim of this project is to increase the value of ECG as a tool for cardiac diagnosis by developing methods for solving the inverse problem of electrocardiography.The first research lead will concentrate on an increase in the reconstruction quality using model-based spatio-temporal regularization techniques. The second approach aims to assess the capability of electro- and magnetocardiographic imaging of reconstructing clinically relevant information in the presence of incomplete models and data. In a third lead, the focus will be on new mathematical approaches like making the solver faster and more effective, which can be achieved by a decomposition of the large-scale ill-conditioned problem. Another aspect is the use of different error norms and regularization schemes.

在心电图（ECG）中，通过解释在体表测量的电压来研究心脏。通过增加更多的传感器和借助物理和数学对信号进行建模，可以提高ECG的空间精度。在心电图逆问题中，目的是从体表的测量信号中估计心脏表面上的电势或心脏电源的来源。这个问题是不适定的：简化假设、先验信息和正则化是必需的。本项目的目的是通过开发解决心电图逆问题的方法来提高心电图作为心脏诊断工具的价值。第一个研究领导将集中在使用基于模型的时空正则化技术来提高重建质量。第二种方法旨在评估心电图和心磁图成像在存在不完整模型和数据的情况下重建临床相关信息的能力。在第三个方面，重点将放在新的数学方法上，例如使求解器更快，更有效，这可以通过分解大规模病态问题来实现。另一个方面是使用不同的误差范数和正则化方案。