High Accuracy Image Reconstruction Using Microwave Measurements from Bio-Matched Antennas and Deep Learning: A Synthesized X-ray Computed Tomography Approach

使用生物匹配天线和深度学习的微波测量进行高精度图像重建：一种合成 X 射线计算机断层扫描方法

• 批准号: 2244882
• 负责人: Asimina Kiourti
• 金额: $ 46万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2244882&HistoricalAwards=false
• 关键词: Accuracy; Image; Reconstruction; Using; Microwave

Several technologies are clinically available to image biological tissues, each with their own merits and limits. Focusing on stroke, the application of interest in this proposal, X-ray computed tomography (CT) and magnetic resonance imaging (MRI) are typically used. Though the spatial resolution is excellent, their hardware is bulky and not suitable for bedside applications. Furthermore, the ability to differentiate between ischemic and hemorrhagic strokes in the ambulance or on-site and for bedside monitoring will have significant potential to improve outcomes and reduce mortality. In this context, microwave tomography is a promising imaging modality, yet it suffers from poor imaging resolution that restricts its clinical use. In this research, an expansion of the fundamental limits of microwave tomography resolution is proposed via an alternative imaging modality that combines the advantages of X-ray CT (high resolution) and microwave tomography (non-ionizing, low-cost, portable). The approach uses non-ionizing microwave measurements and a deep learning neural network to estimate data that would have been collected by an X-ray CT scanner at different angles around the patient. We expect the science developed in this research to be of great use in myriads of healthcare applications (imaging, radiometry, implant telemetry/powering, ablation, etc.) and beyond (e.g., industrial imaging applications). In addition to the intellectual advances, the proposed research is expected to be of significant interest to students and the public. Through interdisciplinary education and diverse recruitment efforts, we intend to expose new audiences to STEM concepts via workshops and family-friendly outings.The proposed research leverages advances in: (a) deep learning to synthesize X-ray CT projection data while relying solely on non-ionizing microwave tomography measurements, and (b) new classes of into-body radiating antennas, namely bio-matched antennas, with unprecedented efficiency of electromagnetic wave propagation towards human body. With the estimated CT projection data in hand, images can be reconstructed using standard CT reconstruction methods, such as filtered back projection. These images are referred to as synthesized CT and an improvement of more than two times over current state-of-the-art peak signal to noise ratio (PSNR) is targeted to provide good image reconstruction. Without loss of generality, focus is on stroke as an example application. The specific goals are: (1) developing a deep learning neural network to learn the complex relationship between microwave tomography measurements and X-ray CT projection data using synthetic/simulation data and line sources in two dimensions, (2) developing a theoretical modeling and experimental framework for bio-matched antennas with unprecedented efficiency of electromagnetic wave transmission towards human body while also being versatile for diverse applications, (3) integrating the deep learning neural network with optimized bio-matched antennas by considering three dimensional scenarios and building a prototype head imager for validation on head phantoms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

临床上有几种技术可用于对生物组织进行成像，每种技术都有自己的优点和局限性。集中在中风，这是本方案中感兴趣的应用，通常使用X射线计算机体层摄影(CT)和磁共振成像(MRI)。虽然空间分辨率很好，但他们的硬件很笨重，不适合床边应用。此外，在救护车或现场区分缺血性中风和出血性中风并进行床边监测的能力将对改善预后和降低死亡率具有重大潜力。在这种背景下，微波断层成像是一种很有前途的成像方式，但它的成像分辨率较差，限制了其临床应用。在这项研究中，通过结合X射线CT(高分辨率)和微波层析(非电离、低成本、便携)的优点的另一种成像方式，提出了扩展微波层析成像分辨率的基本极限。该方法使用非电离微波测量和深度学习神经网络来估计X射线CT扫描仪在患者周围不同角度收集的数据。我们希望在这项研究中开发的科学将在无数的医疗应用中发挥巨大作用(成像、辐射测量、植入物遥测/供电、消融等)。以及更远的领域(例如，工业成像应用)。除了智力上的进步，这项拟议的研究预计将引起学生和公众的极大兴趣。通过跨学科的教育和不同的招募工作，我们打算通过工作坊和适合家庭的郊游让新的受众接触STEM概念。拟议的研究利用以下方面的进展：(A)深度学习在完全依赖非电离微波断层扫描测量的情况下合成X射线CT投影数据，以及(B)新型体内辐射天线，即生物匹配天线，具有前所未有的电磁波向人体传播的效率。有了估计的CT投影数据，就可以使用标准的CT重建方法重建图像，例如滤波反投影。这些图像被称为合成CT，目标是比目前最先进的峰值信噪比(PSNR)提高两倍以上，以提供良好的图像重建。在不失一般性的前提下，我们将重点介绍笔划作为示例应用程序。具体目标是：(1)开发深度学习神经网络，利用合成/模拟数据和二维线源来学习微波断层扫描测量和X射线CT投影数据之间的复杂关系；(2)开发生物匹配天线的理论建模和实验框架，具有前所未有的向人体传输电磁波的效率，同时具有多种用途，(3)将深度学习神经网络与优化的生物匹配天线相结合，考虑三维场景并构建用于验证头部模体的原型头部成像器。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。