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组成结构（即，“看到里面”）。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许多可能的成像算法之一。我的研究项目主要集中在开发和应用新的和改进的波场成像算法到两个应用领域：乳腺癌检测和监测，以及粮食储存监测。虽然这些应用需要不同的硬件系统，但它们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，并且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。在实践中，真正的目标是未知的，验证某些目标的算法并不意味着它将产生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，询问的频率，假定的先验信息），其可用于产生相同目标的多个图像。如果这些图像相似，信心就会增加;如果它们不同，信心就会减弱。 该研究计划致力于建立波场图像的信心。我将创建一个框架，波场成像用户可以在其中生成波场图像和相关的图像置信度图。将通过有效地生成一组参数多样的目标图像并应用图像融合/比较和图像敏感性分析来建立置信度。该框架正视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缺失步骤。临床上强大和可靠的微波乳腺癌成像将提供一个低成本，低风险，微创的大规模筛查工具。 射频谷物存储监测是一项由美国首创的潜在颠覆性技术，有望成为保护我们存储谷物的全球标准。该计划将在应用数学，计算，波动物理，逆问题，机器学习和优化领域培训9名HQP。这些HQP配备了波场成像的基本技能，是填补加拿大这个不断发展的行业中存在的技能缺口的理想选择。