Establishing Confidence in Wavefield Images for Agricultural and Biomedical Applications

建立对农业和生物医学应用波场图像的信心

• 批准号: RGPIN-2020-05677
• 负责人: Jeffrey, Ian
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717915
• 关键词: Establishing; Confidence; Wavefield; Images; Agricultural

Wavefield imaging uses electromagnetic or acoustic wave interrogation to non-invasively reconstruct the constitutive structure of a target (i.e., “see inside”). A wavefield imaging system can be divided into two parts: the hardware required to interrogate a target and collect data, and one of many possible imaging algorithms used to convert the data into an image of the target. My research program focuses on developing and applying novel and improved wavefield imaging algorithms to two application areas: breast cancer detection and monitoring, and grain storage monitoring. While these applications require different hardware systems, they are solved using the same algorithms. Wavefield imaging is a very challenging problem, and the quality of an image is both target and algorithm-parameter dependent. Validation of imaging systems has historically relied on comparing images to the true target. In practice the true target is unknown, and validating an algorithm for some targets does not imply that it will produce accurate images of all potential targets. Fortunately, imaging algorithms have a number of tunable parameters (e.g., frequency of interrogation, assumed prior information), that can be used to produce multiple images of the same target. If these images are similar, confidence grows; if they differ, confidence wanes. This research program addresses establishing confidence in wavefield images. I will create a framework in which wavefield imaging users can generate both a wavefield image and an associated image confidence map. Confidence will be established by efficiently generating a parameter-diverse set of target images and applying image fusion/comparison and image sensitivity analysis. This framework squarely addresses the critical missing step of image validation needed for wavefield imaging users to adopt the technology as a decision-directing tool. Clinically robust and dependable microwave breast cancer imaging will provide a low-cost, low-risk, minimally invasive mass-screening tool. Radio-frequency grain storage monitoring is a Canadian-pioneered potentially disruptive technology that is poised to become a global standard for securing our stored grain. The program will train nine HQP in the areas of applied mathematics, computations, wave physics, inverse problems, machine learning and optimization. These HQP, equipped with essential skills in wavefield imaging, are ideal to fill a skills gap that exists in this growing Canadian industry.

波场成像使用电磁波或声波询问来非侵入性地重建目标的构成结构（即“看到内部”）。波场成像系统可以分为两部分：询问目标和收集数据所需的硬件，以及用于将数据转换为目标图像的多种可能的成像算法之一，我的研究项目重点是开发和应用新颖且改进的波场成像算法到两个应用领域：乳腺癌检测和监测以及谷物储存监测。这些应用需要不同的硬件系统，但使用相同的算法来解决。 波场成像是一个非常具有挑战性的问题，图像的质量既取决于目标又取决于算法参数，成像系统的验证历来依赖于将图像与真实目标进行比较，而在实践中，真实目标是未知的，并验证算法。对于某些目标并不意味着它将生成所有潜在目标的准确图像，幸运的是，成像算法具有许多可调参数（例如询问频率、假设的先验信息），可用于生成相同的多个图像。如果这些图像。相似，信心就会增强；如果不同，信心就会减弱。 该研究计划致力于建立波场图像的置信度，在该框架中，波场成像用户可以生成波场图像和相关的图像置信度图，通过有效生成参数多样化的目标图像集并应用来建立置信度。该框架直接解决了波场成像用户采用该技术作为决策指导工具所需的图像验证的关键步骤，这将提供一种低成本的方法。 ,射频粮食储存监测是一种低风险、微创的大规模筛查工具，是加拿大首创的潜在颠覆性技术，有望成为确保我们储存的粮食安全的全球标准。这些 HQP 具备波场成像方面的基本技能，非常适合填补这一不断发展的加拿大行业中存在的技能缺口。