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.

项目摘要 电磁（EM）成像已被证明具有成为强大的医学成像的巨大潜力 技术.近年来微波、毫米波和射频技术的发展 几十年来已经证明了在诸如脑卒中识别和卒中类型等领域的应用 在一些实施例中，所述方法包括：检测膀胱癌、乳腺癌鉴别、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、非电离辐射和具有成本效益的/便携式形式 因素不幸的是，EM成像具有限制其临床实用性的两个关键缺点：1）传统的 重建模型不能有效地解释复杂和非均匀介质中的微波散射 生物组织;以及2）当前最先进的天线阵列受到个体的物理尺寸的限制 天线元件，其不允许在对象周围密集地或最佳地捕获测量。这 显著地降低了成像分辨率，并且可能妨碍病变尺寸和形状的精确可视化。这 该提案开发了一种新的EM成像范例，可以从一组 优化的天线位置，以大大提高三维电磁成像能力。我们的计划将 包括两个主要组成部分：1）计算框架将制定重建三维 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，并将利用基于优化和机器学习的工具来建模 通过生物组织的微波散射; 2）将开发专门的天线阵列， 微波散射测量，与个别天线元件定位在超高 密度或优化的非规则间距。非规则间隔阵列中的天线间隔将是 基于识别超高密度阵列内具有更大影响的天线位置来计算 在三维介电常数重建上的优势。识别这些位置将使3D介电常数能够被 以较少的测量和较少的重建时间精确地重建。这种新方法的效用 将在头部成像和 区分中风类型和乳腺癌，以检测和监测癌症。在这个提议中，测试案例是使用 微波来重建3D介电常数（其是组织水含量的指示，并且被修改为 由疾病状态），但是这种范例可以容易地扩展到电磁频谱的其他区域。 总的来说，该项目将实现高分辨率成像，准确定位病变大小和形状 这对于EM成像的成功临床转化至关重要。该项目的成果将适用于 EM成像的多种临床应用，从癌症检测，个性化治疗进展 监测、量化炎症，以及实时跟踪热疗法。