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Inverse Problems with Internal Data

内部数据的反问题

基本信息

批准号：
1912821
负责人：
John Schotland
金额：
$ 39.37万
依托单位：
Regents of the University of Michigan - Ann Arbor
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：
2019-07-01 至 2020-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1912821&HistoricalAwards=false
关键词：
Inverse Problems Internal Data

项目摘要

Advances in imaging technologies such as computed tomography (CT), magnetic resonance imaging (MRI) and super resolution microscopy have transformed the practice of clinical medicine and basic biomedical research. Although the development of such technologies is well known to depend upon progress in physics and engineering, it is less well known that applied and computational mathematics has also played an essential role. This research project studies mathematical questions that arise in new medical biomedical imaging modalities in which new novel measurements play a key role. The research will study novel mathematical algorithms that will lead to improvements in optical imaging both with respect to resolution (visualizing structures at smaller scales) and computational speed. In particular, the project aims to devise robust and accurate image reconstruction algorithms that may lead to the detection and characterization of disease at much earlier stages than is currently possible. The principal investigator has research interests in applied mathematics and theoretical physics. He is also a physician. Graduate students will be trained to function in this interdisciplinary environment.The objective of this project is to investigate inverse problems with internal data that arise in biomedical optical imaging. Two classes of problems will be considered. (i) Teh PI will develop mathematically-justified methods for imaging below the diffraction limit of resolution, also known as superresolution imaging. The proposed work includes both analysis of the inverse scattering problem with internal sources and the development of reconstruction algorithms. The algorithms will be tested and characterized using data from physically realistic numerical simulations. (ii) The PI will study inverse problems that arise in acousto-optic imaging. The research will focus on the regime of coherent multiple scattering which leads to considerable mathematical simplifications compared to incoherent imaging. In particular, the PI will develop reconstruction methods for recovering the absorption and scattering coefficients of the radiative transport equation from coherent acousto-optic measurements. Finally, the role of improvements in modeling of the acousto-optic effect on image reconstruction will be investigated.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.

计算机断层扫描（CT）、磁共振成像（MRI）和超分辨率显微镜等成像技术的进步改变了临床医学和基础生物医学研究的实践。虽然众所周知，这些技术的发展取决于物理学和工程学的进步，但人们不太清楚的是，应用数学和计算数学也发挥了重要作用。该研究项目研究了新的医学生物医学成像模式中出现的数学问题，其中新的测量方法发挥了关键作用。该研究将研究新的数学算法，这些算法将导致光学成像在分辨率（在较小尺度下可视化结构）和计算速度方面的改进。特别是，该项目旨在设计强大而准确的图像重建算法，可能导致在比目前可能的更早的阶段检测和表征疾病。主要研究者的研究兴趣是应用数学和理论物理。他也是一名医生。研究生将被训练在这个跨学科的环境中发挥作用。本项目的目标是研究生物医学光学成像中出现的内部数据的逆问题。将考虑两类问题。(i)该PI将开发用于分辨率衍射极限以下成像的方法，也称为超分辨率成像。所提出的工作包括分析与内部源的逆散射问题和重建算法的发展。该算法将进行测试，并使用物理现实的数值模拟数据的特点。(ii)PI将研究声光成像中出现的逆问题。研究将集中在制度的相干多重散射，导致相当大的数学简化相比，非相干成像。特别是，PI将开发用于从相干声光测量中恢复辐射传输方程的吸收和散射系数的重建方法。最后，改进模型的声光效应对图像重建的作用将被调查。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。