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 电磁成像已显示出成为一种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，已在脑卒中识别和中风类型等领域展示了应用 此外，它还提供了多种应用，包括肿瘤鉴别、乳腺癌检测、膀胱状态跟踪和骨质疏松监测等。埃姆 成像的主要优势包括深层组织成像、非电离辐射和经济实惠/便携的形式 各种因素。不幸的是，EM成像有两个限制其临床应用的关键缺点：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人物理尺寸的限制 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 极大地降低了成像分辨率，并可能妨碍准确显示病变大小和形状。这 Proposal开发了一种新的电磁成像范例，其中测量可以从一组 优化天线位置，大幅增强3D EM成像能力。我们提议的项目将 包括两个主要部分：1)将制定计算框架以重建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 在大数据计算方面取得了进展，并将利用基于优化和机器学习的工具来建模 微波在生物组织中的散射；以及2)将开发专门的天线阵列来收集 微波散射测量，单个天线单元位于超高 密度或优化的非规则间距。非规则间隔阵列中的天线间距将为 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 在三维介电常数重建方面优于其他方法。识别这些位置将使3D介电常数 精确重建，测量更少，重建时间更短。这一新方法的实用性 将通过面向应用程序的测试在头部成像到 区分中风类型和乳房，以检测和监测癌症。在这个提案中，测试用例是关于使用 用于重建3D介电常数的微波(这是组织水分含量的指示器，经过修改 疾病状态)，但这种模式可以很容易地扩展到电磁频谱的其他区域。 总体而言，该项目将使高分辨率成像能够准确定位病变大小和形状 这对于EM成像的成功临床翻译至关重要。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、个性化治疗的进展 监测、量化炎症，到实时跟踪热疗。