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Development, analysis and application of mathematical methods for Magnetic Particle Imaging (MathMPI)

磁粒子成像数学方法（MathMPI）的开发、分析和应用

基本信息

批准号：
262269551
负责人：
Dr. Wolfgang Erb
金额：
--
依托单位：
Department of Mathematics
依托单位国家：
德国
项目类别：
Scientific Networks
财政年份：
2014
资助国家：
德国
起止时间：
2013-12-31 至 2015-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/262269551?language=en
关键词：
Development analysis application mathematical methods

项目摘要

Magnetic Particle Imaging (MPI) is an emerging imaging modality that determines the spatial distribution of magnetic nanoparticles by measuring the non-linear magnetization response of the particles to an applied magnetic field. MPI offers a high dynamic spatial and temporal resolution and, in contrast to other tomographic methods, it does not employ any ionizing radiation. This makes MPI a very promising imaging modality for biomedical diagnostics. This project addresses for the first time the systematic study of mathematical questions related to MPI. The central goal of this network is the development, analysis and application of mathematical methods to improve the reconstruction quality in MPI. In particular, taylored to the specific needs of MPI we develop elaborate reconstruction algorithms, analyze and refine the underlying MPI models and test the new methods numerically on real biomedical data. To achieve these goals this network provides an interdisciplinary platform for researchers from various scientific fields including applied mathematics, modeling, image processing, medical physics as well as electrical engineering. Two of the meetings organized in this network are planned in form of study groups. This format is particularly suitable for interdisciplinary teamwork, generating synergies and strengthen the communication within the network.

磁性粒子成像（MPI）是一种新兴的成像模式，其通过测量粒子对所施加的磁场的非线性磁化响应来确定磁性纳米粒子的空间分布。MPI提供了高动态空间和时间分辨率，与其他断层扫描方法相比，它不使用任何电离辐射。这使得MPI成为生物医学诊断的一种非常有前途的成像模式。该项目首次系统地研究了与MPI相关的数学问题。该网络的中心目标是开发，分析和应用数学方法来提高MPI中的重建质量。特别是，taylored到MPI的具体需求，我们制定了详细的重建算法，分析和完善底层的MPI模型和测试的新方法数值上的真实的生物医学数据。为了实现这些目标，该网络为来自各个科学领域的研究人员提供了一个跨学科的平台，包括应用数学，建模，图像处理，医学物理以及电气工程。该网络组织的两次会议计划以研究小组的形式举行。这种形式特别适合跨学科的团队合作，产生协同作用，加强网络内的沟通。