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.

临床上可使用多种技术对生物组织进行成像，每种技术都有各自的优点和局限性。聚焦于中风，本提案中感兴趣的应用，通常使用 X 射线计算机断层扫描 (CT) 和磁共振成像 (MRI)。尽管空间分辨率非常好，但它们的硬件体积庞大，不适合床边应用。此外，在救护车或现场以及床边监测中区分缺血性和出血性中风的能力将具有改善结果和降低死亡率的巨大潜力。在这种情况下，微波断层扫描是一种有前途的成像方式，但其成像分辨率较差限制了其临床应用。在这项研究中，提出了通过结合 X 射线 CT（高分辨率）和微波断层扫描（非电离、低成本、便携式）优点的替代成像方式来扩展微波断层扫描分辨率的基本极限。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。我们预计这项研究中开发的科学将在无数医疗保健应用（成像、辐射测量、植入物遥测/供电、消融等）及其他应用（例如工业成像应用）中发挥巨大作用。除了智力进步之外，拟议的研究预计也会引起学生和公众的极大兴趣。通过跨学科教育和多样化的招聘工作，我们打算通过研讨会和家庭友好型郊游向新受众展示 STEM 概念。拟议的研究利用了以下方面的进展：(a) 深度学习来合成 X 射线 CT 投影数据，同时仅依靠非- 电离微波断层扫描测量，以及（b）新型体内辐射天线，即生物匹配天线，具有前所未有的向人体传播电磁波的效率。有了估计的 CT 投影数据，就可以使用标准 CT 重建方法（例如滤波反投影）来重建图像。这些图像被称为合成 CT，其峰值信噪比 (PSNR) 比当前最先进的峰值信噪比 (PSNR) 提高两倍以上，旨在提供良好的图像重建。不失一般性，重点关注中风作为示例应用。具体目标是：(1) 开发深度学习神经网络，以使用二维合成/模拟数据和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) 开发理论模型和生物匹配天线的实验框架，具有前所未有的向人体传输电磁波的效率，同时也具有多种应用的多功能性，（3）通过考虑三维场景并构建原型头部，将深度学习神经网络与优化的生物匹配天线集成用于头部验证的成像仪该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。