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

项目摘要/摘要 根据美国疾病控制与预防中心的数据，美国每40秒就有一例中风，每4分钟就有一例死亡 65岁及以上的幸存者中，有一半以上的人行动不便。有能力 在院前设置中区分缺血性/出血性中风并监测中风的演变 床边有很大的潜力来改善结果和降低死亡率。不幸的是，目前的实践状况 MRI和CT系统体积庞大，价格昂贵，限制了成像只能在临床环境下进行，时间间隔也很稀疏。CT 还使用构成安全风险的电离辐射，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，但尚未实现 在临床环境中使用。这主要是由于其空间分辨率较差，因为要素维度 与电磁波的波长相当。不幸的是，降低波长(即， 增加MTI的测量频率)是不可行的，因为高频容易产生噪声和严重的 组织内的衰减。相反，我们的目标是探索扩大基本限制的可行性 通过创新的MTI分辨率从低频测量中估计高频数据 深度神经网络(DNN)。我们的目标是检测符合临床预期的中风&lt；1 cm×1 cm 这是对院前设置和整个中风监测过程中亟需的补充。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 我们可以用来为任何给定的低频增加最高可用频率的域 测量。假设2：人为地将频率提高N倍将使图像分辨率提高N倍 次数，与使用的MTI重建方法无关。这里，N取决于最高可用频率(至 被确定)，并且预计至少等于2。这项研究意义重大，因为它揭示了 在生物介质中提高MTI分辨率的以前未知的知识。在目标1中，我们将制定 使用2D/3D解算器、规范/解剖头部模型和新型体内辐射天线的DNN 以前所未有的效率。我们的研究将验证假设1。在目标2中，我们将验证DNN 通过使用估计的高频数据来数值地重建图像。我们的研究将验证 假设2。在目标3中，我们将使用组织模拟模型对DNN进行实验验证。成功 与最先进的MTI重建相比，重建需要提高(N倍以上)的图像分辨率 相同的测量频率。使用实际和估计的图像重建精度的比较 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 人类受试者。我们认为，这项技术将成为克服上呼吸道疾病的一个非常必要的突破。 MTI算法在脑内各种诊断和/或院前评估应用中的频率限制 斯托克应用程序和其他应用程序。

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.