Bridging the gap between Computations and Experiment in Electromagnetic Inversion

弥合电磁反演计算与实验之间的差距

• 批准号: RGPIN-2020-05674
• 负责人: Gilmore, Colin
• 金额: $ 2.04万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752105
• 关键词: Bridging; gap; between; Computations; Experiment

The ability to see things that are normally hidden from the naked eye is an enormous benefit to humankind. Consider medical imaging (detecting and monitoring disease) or geo-prospecting/monitoring (detecting economically valuable natural resources). All these benefits require being able to `see' inside of normally inaccessible regions, like the human body or underground. Electromagnetic inversion uses electromagnetic (e.g. radio) waves and imaging algorithms to see inside a wide variety of objects. Images are created using data-collection hardware (antennas and radio transceivers), computer models, and optimization algorithms. Importantly, these images are quantitative with each pixel having a number associated with the physics of the material. Applications of electromagnetic inversion are extremely broad: it can be used to image targets such as breast cancer, stored grain, as well as oil-fields. Imaging these diverse targets occurs with virtually identical processes and algorithms, thus this research program proposes fundamental advances that will apply to multiple socio-economic and environmental needs. Electromagnetic inversion uses more mathematically complex algorithms to image interiors of objects than some other imaging technologies (e.g. x-rays), but for many applications (e.g. grain bins) it remains the only way to generate information about target interiors. Electromagnetic inversion presents a challenging research problem, but its potential has made it an active area of research. The long-term vision of my proposed research program is to create electromagnetic inversion systems and associated software to image the inside objects and regions where such information is valuable. The research program will take an engineering approach to imaging (a) stored-grain in order to maximize crop value and minimize losses due to spoilage, (b) biomedical imaging, particularly the detection and monitoring of breast cancer. The barriers facing the deployment of such imaging systems are solvable through good engineering research, and these technologies will be made practical by: (1) reducing modeling error - the difference between the actual system and the computational model of the system; and (2) advancing the inversion algorithm workflows that generate the images from these systems. This research program will advance imaging systems monitoring Canadian grain and create a series of prototype biomedical imaging systems with higher resolution than existing technologies. Hardware, software, and algorithms will all be developed, but the main novel contributions will be in the systems-level integration, workflows and calibration. The benefits of solving these problems are clear: higher quality (and more) grain for Canadian producers and exporters, or the ability to image cancers with non-ionizing radiation. The benefits to Canada will include research advances, HQP training, direct employment, larger exports, and development of new technologies.

能够看到肉眼通常看不到的东西对人类来说是一个巨大的好处。考虑医学成像（检测和监测疾病）或地质勘探/监测（检测经济上有价值的自然资源）。所有这些好处都需要能够“看到”通常无法进入的区域内部，比如人体或地下。电磁反演利用电磁波（例如无线电波）和成像算法来观察各种物体的内部。使用数据收集硬件（天线和无线电收发器）、计算机模型和优化算法创建图像。重要的是，这些图像是定量的，每个像素都有一个与材料物理相关的数字。电磁反演的应用非常广泛：它可以用于成像目标，如乳腺癌，储存的粮食，以及油田。这些不同目标的成像过程和算法几乎相同，因此该研究计划提出了适用于多种社会经济和环境需求的基本进展。电磁反演使用比其他成像技术（如x射线）更复杂的数学算法来成像物体内部，但对于许多应用（如粮仓），它仍然是生成目标内部信息的唯一方法。电磁反演是一个具有挑战性的研究问题，但其潜力使其成为一个活跃的研究领域。我提出的研究计划的长期愿景是创建电磁反演系统和相关软件，以成像内部物体和区域，这些信息是有价值的。该研究项目将采用工程方法来成像(a)储存谷物，以最大限度地提高作物价值并减少因腐败造成的损失；(b)生物医学成像，特别是乳腺癌的检测和监测。通过良好的工程研究，可以解决部署此类成像系统所面临的障碍，这些技术将通过以下方式实现：(1)减少建模误差-实际系统与系统计算模型之间的差异；(2)提出了从这些系统生成图像的反演算法工作流程。该研究项目将推进监测加拿大谷物的成像系统，并创建一系列比现有技术分辨率更高的生物医学成像系统原型。硬件、软件和算法都将被开发，但主要的新贡献将在系统级集成、工作流程和校准方面。解决这些问题的好处是显而易见的：为加拿大的生产商和出口商提供更高质量（和更多）的粮食，或者用非电离辐射成像癌症的能力。对加拿大的好处将包括研究进展、HQP培训、直接就业、更大的出口和新技术的开发。