Data driven inversion methods and image reconstruction for nonlinear media

非线性介质的数据驱动反演方法和图像重建

• 批准号: 2308200
• 负责人: Shari Moskow
• 金额: $ 27万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2308200&HistoricalAwards=false
• 关键词: Data; driven; inversion; methods; image

This project involves the development of new methods for determining internal material parameters from external measurements, with a focus on radar applications, medical imaging, and optical design. In the new algorithms, a key innovation lies in using the data itself to generate a compact model for wave propagation, resulting in highly efficient image reconstruction. The applicability of these algorithms will be expanded to handle large and noisy data sets, complex media, and scenarios where sources and receivers are not collocated—a significant limitation in existing reduced model approaches. Furthermore, novel methodologies will be developed for the reconstruction of materials exhibiting nonlinear wave propagation characteristics. By reducing computational costs, the methods developed in this project will enable improved imaging in large-scale situations. Several students will be trained over the course of this research, and plans to promote diversity and inclusion in research are integrated into student recruitment.This research will overcome challenges to image reconstruction by improving some of the most promising direct methods for inversion. This includes (i) expanding data driven reduced order model imaging methods to very general classes of data sets, models with nonlocal operators and transmission problems, while at the same time establishing a rigorous foundation, (ii) developing novel inversion methods for coefficient determination when the underlying forward model is nonlinear and (iii) imaging general linear and nonlinear anisotropy in the hybrid method Magnetoacoustic Tomography with Magnetic Induction. The novel reduced order model approaches will be developed to handle large and noisy data sets, problems in which source and receiver are not collocated, and imaging problems with dispersion. The inverse Born series will be adapted to handle inversion where the forward problem is nonlinear, and its application will rely on the description of the forward series and will involve a convergence analysis. The well-posedness of the system of transport equations for Magnetoacoustic Tomography with Magnetic Induction imaging of general anisotropy will be established using carefully defined inflow boundary conditions.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.

该项目涉及开发从外部测量中确定内部材料参数的新方法，重点是雷达应用、医学成像和光学设计。在新算法中，一个关键的创新在于使用数据本身来生成波传播的紧凑模型，从而实现高效的图像重建。这些算法的适用性将被扩展到处理大型和嘈杂的数据集，复杂的媒体，以及源和接收器没有搭配的场景-这是现有简化模型方法中的一个重大限制。此外，将开发新的方法来重建具有非线性波传播特性的材料。通过降低计算成本，该项目开发的方法将改善大规模情况下的成像。几名学生将在这项研究的过程中接受培训，并计划促进研究的多样性和包容性纳入学生招聘。本研究将通过改进一些最有前途的直接反演方法来克服图像重建的挑战。这包括(i)将数据驱动的降阶模型成像方法扩展到非常一般的数据集、具有非局部算子和传输问题的模型，同时建立严格的基础；（ii）开发新的反演方法，用于在底层正演模型为非线性时确定系数；（iii）在磁感应磁声层析成像混合方法中成像一般线性和非线性各向异性。将开发新的降阶模型方法来处理大型和有噪声的数据集，源和接收器未配置的问题，以及具有色散的成像问题。逆玻恩级数将适用于处理非线性正演问题的反演，它的应用将依赖于对正演级数的描述，并将涉及收敛分析。利用仔细定义的流入边界条件，将建立具有一般各向异性磁感应成像的磁声层析成像输运方程系统的适定性。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。