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.

在心电图学中，心脏是通过解释在体表上测量的电压来研究的。通过增加更多的传感器，并借助物理和数学对信号进行建模，可以提高心电的空间精度。在心电反问题中，目的是根据体表的测量信号来估计心脏电源的来源或心脏表面的电位。这个问题是不适定的：需要简化假设、先验信息和正则化。本项目的目的是通过开发解决心电逆问题的方法来增加心电作为心脏诊断工具的价值。第一个研究线索将集中在利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在存在不完整的模型和数据的情况下，心电和心磁图成像重建临床相关信息的能力。在第三条线索中，重点将放在新的数学方法上，比如让解算器更快、更有效，这可以通过分解大规模的病态问题来实现。另一个方面是使用不同的误差规范和正则化方案。