Computational Framework to Enhance Antenna-based Electromagnetic Imaging

增强基于天线的电磁成像的计算框架

• 批准号: 10667975
• 负责人: Shwetadwip Chowdhury
• 金额: $ 41.78万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-21 至 2025-09-20
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10667975
• 关键词: 3-Dimensional; Beds; Big Data; Biological; Bladder; Brain; Breast; Breast Cancer Detection; Cancer Detection; Clinical; Collection; Complex; Consumption; Coupling; Data; Data Set; Development; Disadvantaged; Disease; Electromagnetic Fields; Electromagnetics; Elements; Extremely High Frequency Radio Waves; Head; Health; Image; Imaging technology; Individual; Inflammation; Laws; Lesion; Localized Lesion; Location; Machine Learning; Malignant Neoplasms; Measurement; Medical Imaging; Microscopy; Modeling; Monitor; Nonionizing Radiation; Optics; Osteoporosis; Outcome; Personal Satisfaction; Phase; Positioning Attribute; Research; Resolution; Shapes; Stroke; Technology; Testing; Time; Tissue imaging; Tissues; Training; Variant; Visualization; Water; clinical application; clinical translation; computer framework; cost; cost effective; density; design; electric field; healing; health assessment; high resolution imaging; imaging capabilities; microwave electromagnetic radiation; neural network; novel strategies; personalized medicine; portability; prevent; prototype; radio frequency; reconstruction; tool; transmission process

Project Summary Electromagnetic (EM) imaging has demonstrated significant potential to become a powerful medical imaging technology. Developments in microwave, millimeter-wave, and radio-frequency technology over the last few decades have demonstrated applications in fields such as brain stroke identification and stroke-type differentiation, breast cancer detection, bladder state tracking, and osteoporosis monitoring, among others. EM imaging’s key advantages include deep tissue imaging, non-ionizing radiation, and cost-effective/portable form factors. Unfortunately, EM imaging suffers from two key disadvantages that limit its clinical utility: 1) traditional reconstruction models cannot efficiently account for microwave scattering in complex and heterogeneous biological tissues; and 2) current state-of-the-art antenna arrays are limited by the physical size of individual antenna elements, which do not allow measurements to be densely or optimally captured around an object. This drastically reduces imaging resolution, and can prevent accurate visualization of lesion size and shape. This proposal develops a new EM imaging paradigm where measurements can be collected from a set of optimized antenna locations to drastically enhance 3D EM imaging capabilities. Our proposed project will include two major components: 1) computational frameworks will be formulated to reconstruct 3D permittivity from noninvasive microwave scattering measurements. These frameworks will leverage recent advances in big-data computing, and will utilize optimization-based and machine-learning tools to model microwave scattering through biological tissue; and 2) specialized antenna arrays will be developed to collect microwave scattering measurements, with individual antenna elements positioned at either ultra-high densities or optimized non-regular spacings. The antenna spacings in the non-regular spaced array will be computed based on identifying antenna-positions within the ultra-high-density array that have a greater effect than others on 3D permittivity reconstruction. Identifying these positions will enable 3D permittivity to be accurately reconstructed with fewer measurements and less reconstruction time. The utility of this new approach will be demonstrated through application-oriented testing in the context of both imaging of the head to differentiate stroke-type and of the breast to detect and monitor cancer. In this proposal, the test-case is on using microwaves to reconstruct 3D dielectric permittivity (which is an indicator for tissue water content, and is modified by disease state), but this paradigm can readily be extended to other regions of the electromagnetic spectrum. Overall, this project will enable the high resolution imaging with accurate localization of lesion size and shape that is pivotal for successful clinical translation of EM imaging. The outcomes of this project will be applicable to the diverse clinical applications of EM imaging, spanning from cancer detection, personalized treatment progress monitoring, quantification of inflammation, to real-time tracking of thermal therapies.

项目概要 电磁 (EM) 成像已显示出成为强大的医学成像的巨大潜力 技术。过去几年微波、毫米波和射频技术的发展 几十年来已经在脑卒中识别和卒中类型等领域展示了应用 分化、乳腺癌检测、膀胱状态跟踪和骨质疏松症监测等。 EM 成像的主要优势包括深层组织成像、非电离辐射和经济高效/便携式形式 因素。不幸的是，电磁成像有两个限制其临床应用的主要缺点：1）传统 重建模型无法有效地解释复杂和异构的微波散射 生物组织； 2）当前最先进的天线阵列受到个体物理尺寸的限制 天线元件，不允许在物体周围密集或最佳地捕获测量结果。这 大大降低了成像分辨率，并且会妨碍准确显示病变大小和形状。这 该提案开发了一种新的电磁成像范例，可以从一组 优化的天线位置可显着增强 3D EM 成像能力。我们提出的项目将 包括两个主要组成部分：1）将制定计算框架来重建 3D 非侵入性微波散射测量的介电常数。这些框架将利用最近的 大数据计算的进步，并将利用基于优化的机器学习工具进行建模 微波通过生物组织散射； 2）将开发专门的天线阵列来收集 微波散射测量，各个天线元件位于超高 密度或优化的不规则间距。非规则间隔阵列中的天线间距为 基于识别超高密度阵列中具有更大影响的天线位置来计算 在 3D 介电常数重建方面优于其他人。确定这些位置将使 3D 介电常数 通过更少的测量和更少的重建时间来准确地重建。这种新方法的实用性 将通过头部成像背景下的面向应用的测试进行演示 区分中风类型和乳腺癌，以检测和监测癌症。在这个提案中，测试用例是使用 微波重建 3D 介电常数（这是组织含水量的指标，并经过修改 疾病状态），但这种范例可以很容易地扩展到电磁频谱的其他区域。 总体而言，该项目将实现高分辨率成像，并准确定位病变大小和形状 这对于电磁成像临床转化的成功至关重要。该项目的成果将适用于 电磁成像的多样化临床应用，从癌症检测到个性化治疗进展 监测、炎症量化，以及热疗法的实时跟踪。