权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Analytical and Computational Approaches for Quantitative Tomography of Tissue

组织定量断层扫描的分析和计算方法

基本信息

批准号：
1907097
负责人：
Alexandru Tamasan
金额：
$ 20万
依托单位：
The University of Central Florida Board of Trustees
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1907097&HistoricalAwards=false
关键词：
Analytical Computational Approaches Quantitative Tomography

项目摘要

This project will develop new non-invasive quantitative tomographic methods by imaging the electrical properties of biological matter, based on coupled physics inverse problems. The research holds promise to enable new technologies for decoding data used in current medical diagnostic practices and biomedical research by providing the theoretical basis for new imaging methods of higher accuracy and resolution than existing ones. The quantitative distribution of electrical conductivity and permittivity is known to distinguish a benign tumor from a malignant one, it can apply to monitor the pulmonary function of the lung, the thoracic blood volume, hyperthermia, the gastrointestinal function in newborns in intensive care, etc. This project will advance the understanding of information content in the data and produce quantitative images of biological tissues with anisotropic structures while using an optimal number of measurements. Another facet of this project is the development of robust methods which produce quantitative images of biological structure corresponding to frequencies where contrast is optimal. As a consequence, it will provide new tools in biological research by enabling imaging of biological processes at a smaller scale. During the course of the project, graduate students will be trained in an interdisciplinary area of research. The project's findings will be integrated in a student seminar and a special topics course for Mathematics, Physics, and Engineering students at the University of Central Florida.The project integrates novel advances in the mathematical analysis of nonlinear inverse problems with engineering advances in sensor design and data acquisition and aims to shift the paradigm in some of the current engineering practices. The analytical component of the project lies at the intersection of nonlinear Inverse Problems, Geometry, Optimization, and Geometric Measure theory. The principal investigator (PI) plans to improve the current knowledge of the anisotropic least gradient problems arising in physical models which are close to the actual engineering practices. In particular, the PI seeks to determine the anisotropic structure of biological tissue in reconstruction of two-tensors by employing minimal interior data. Another facet of this project seeks to produce quantitative images of the complex biological structure at radio frequencies by coupling the nonlinear inverse problem techniques for Maxwell electromagnetics with the quantum model of resonance of the magnetic spin. The project also aims to determine the electric conductivity distribution in materials with infinite limiting contrast on graphs or neural networks. Based on the analytical findings, the reconstruction methods will be translated in algorithms and tested on simulated data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将基于耦合物理逆问题，通过成像生物物质的电学特性，开发新的非侵入性定量层析方法。该研究有望为当前医学诊断实践和生物医学研究中使用的解码数据提供新技术，为比现有成像方法更高精度和分辨率的新成像方法提供理论基础。电导率和介电常数的定量分布被认为是区分良恶性肿瘤的重要指标，可用于监护新生儿肺功能、胸血容量、热疗、胃肠功能等方面的监测。该项目将促进对数据中信息内容的理解，并在使用最佳测量次数的同时产生具有各向异性结构的生物组织的定量图像。该项目的另一个方面是开发健壮的方法，生成与对比度最佳的频率相对应的生物结构定量图像。因此，它将为生物研究提供新的工具，使生物过程的成像在更小的规模。在项目过程中，研究生将接受跨学科研究领域的培训。该项目的研究结果将被整合到佛罗里达中央大学的学生研讨会和数学、物理和工程专业学生的专题课程中。该项目将非线性逆问题的数学分析的新进展与传感器设计和数据采集的工程进展相结合，旨在改变当前一些工程实践中的范式。该项目的分析部分位于非线性反问题，几何，优化和几何测量理论的交叉点。首席研究员（PI）计划改进目前在物理模型中出现的各向异性最小梯度问题的知识，使其更接近实际工程实践。特别是，PI试图确定生物组织的各向异性结构，在重建双张量采用最小的内部数据。该项目的另一个方面是通过将麦克斯韦电磁学的非线性反问题技术与磁自旋共振的量子模型相结合，寻求在无线电频率下产生复杂生物结构的定量图像。该项目还旨在通过图形或神经网络的无限极限对比度确定材料中的电导率分布。基于分析结果，重建方法将转化为算法并在模拟数据上进行测试。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。