High Resolution Microwave Tomographic Imaging of Brain Strokes Using Low-Frequency Measurements and Deep Neural Networks

项目概要/摘要 根据CDC的数据，美国每40秒就发生一次中风，每4分钟就有一人死亡 65岁及以上的幸存者中有一半以上的人行动能力下降。的能力 在院前环境中区分缺血性/出血性卒中，并通过 床旁有很大的潜力，以改善结果和降低死亡率。不幸的是， MRI和CT系统体积庞大且价格昂贵，将成像限制在临床设置和稀疏间隔。CT 还使用电离辐射，造成安全风险，并进一步禁止频繁成像。微波 断层成像（MTI）是MRI和CT的一种有前途的替代/补充选择，但还有待于进一步研究。 用于临床环境。这主要是由于其空间分辨率差，因为特征尺寸 与电磁波的波长相当。不幸的是，减小波长（即， 增加MTI的测量频率）是不可行的，因为高频易于产生噪声， 组织内衰减。相反，我们的目标是探索扩大基本限制的可行性， 通过创新从低频测量中估计高频数据的MTI分辨率 深度神经网络（DNN）。我们的目标是检测<1cm×1cm的中风，满足临床预期， 一个非常需要的除了院前设置和整个中风监测过程。假设 1：在生物成像周围测量的低频和高频数据之间存在关系 域，我们可以使用它来“人为地”增加任何给定低频的最高可用频率 测量.假设2：将频率“人为”增加N倍将使图像分辨率提高N 无论使用何种MTI重建方法。这里，N取决于最高可用频率（t0 而两个人，都是注定的，注定的。这项研究很重要，因为它揭示了 用于增强生物介质中MTI分辨率的先前未知的知识。在目标1中，我们将开发 DNN使用2D/3D解算器，规范/解剖头部模型和一类新的体内辐射天线 前所未有的效率。我们的研究将验证假设1。在目标2中，我们将验证DNN 通过使用所估计的高频数据来数字地重建图像。我们的研究将验证 假设2.在目标3中，我们将使用组织仿真幻影实验验证DNN。成功 与最先进的MTI重建相比， 相同的测量频率。使用实际与估计的图像重建精度比较 高频数据将进一步揭示该方法的有效性。可行性将构成未来研究的基础， 人类实验对象我们设想这项技术是一个急需的突破，以克服上层 用于脑中各种诊断和/或院前评估应用的MTI算法的频率限制 斯托克应用程序和超越。

PROJECT SUMMARY / ABSTRACT According to the CDC, a stroke occurs in the United States every 40 seconds, with a fatality every 4 minutes and associated reduction in mobility in more than half of survivors of ages 65 and over. The ability to differentiate ischemic/hemorrhagic strokes in the pre-hospital setting and to monitor stroke evolution by the bedside has the great potential to improve outcomes and reduce mortality. Unfortunately, state-of-the-practice MRI and CT systems are bulky and pricy, restricting imaging to the clinical setting and sparse intervals. CT also uses ionizing radiation that poses safety risks and further prohibits frequent imaging. Microwave Tomographic Imaging (MTI) is a promising alternative/complementary option to MRI and CT, but has yet to be used in the clinical setting. This is mainly due to its poor spatial resolution as feature dimensions are comparable to the wavelength of the electromagnetic wave. Unfortunately, reducing the wavelength (i.e., increasing the measurement frequency) of MTI is not viable as high frequencies are prone to noise and severe attenuation inside tissues. Instead, our goal is to explore the feasibility of expanding the fundamental limits of MTI resolution via innovations in estimating high-frequency data from low-frequency measurements using Deep Neural Networks (DNNs). We target detection of strokes <1cm×1cm that meets clinical expectations for a much needed addition to the pre-hospital setting and throughout the stroke monitoring process. Hypothesis 1: A relationship exists between the low- and high-frequency data measured around a biological imaging domain that we can use to ‘artificially’ increase the highest usable frequency for any given low-frequency measurements. Hypothesis 2: An ‘artificial’ increase in frequency by N times will improve image resolution by N times, regardless of the MTI reconstruction method used. Here, N depends on the highest usable frequency (to be determined) and is expected to be at least equal to two. The study is significant because it reveals previously unknown knowledge for enhancing MTI resolution in biological media. In Aim 1, we will develop the DNN using 2D/3D solvers, canonical/anatomical head models, and a new class of into-body radiating antennas with unprecedented efficiency. Our study will validate Hypothesis 1. In Aim 2, we will validate the DNN numerically by using the estimated high-frequency data to reconstruct the image. Our study will validate Hypothesis 2. In Aim 3, we will validate the DNN experimentally using tissue-emulating phantoms. Successful reconstruction will entail improved (N times higher) image resolution vs. state-of-the-art MTI reconstruction at the same measurement frequency. A comparison of image reconstruction accuracy using actual vs. estimated high-frequency data will further reveal the method’s efficacy. Feasibility will form the basis of future studies on human subjects. We envision this technique to be a much needed breakthrough to overcoming the upper frequency limit of MTI algorithms for various diagnosis and/or pre-hospital assessment applications in brain stoke applications and beyond.