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

使用低频测量和深度神经网络对脑中风进行高分辨率微波断层成像

• 批准号: 10429133
• 负责人: Asimina Kiourti
• 金额: $ 7.88万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10429133
• 关键词: 3-Dimensional; Affect; Algorithms; Ambulances; Anatomy; Area; Biological; Brain; Brain hemorrhage; Brain imaging; Breast Cancer Detection; Calibration; Centers for Disease Control and Prevention (U.S.); Clinical; Data; Data Set; Detection; Development; Diagnosis; Dimensions; Early treatment; Electromagnetic Energy; Electromagnetics; Evaluation; Evolution; Frequencies; Future; Goals; Head; Health; Hemorrhage; Hospitals; Hour; Human; Image; Ionizing radiation; Ischemic Stroke; Knowledge; Magnetic Resonance Imaging; Measurement; Measures; Methods; Modeling; Monitor; Muslim religion; Neurology; Neurons; Noise; Output; Pre-hospital setting; Process; Research; Resolution; Rest; Risk; Safety; Site; Stroke; Study Subject; Survivors; Symptoms; System; Techniques; Testing; Time; Tissue Model; Tissues; Training; United States; Variant; X-Ray Computed Tomography; algorithm training; attenuation; deep neural network; design; dielectric property; diffuse optical tomography; expectation; human old age (65+); human subject; image reconstruction; imaging modality; imaging system; improved; improved outcome; innovation; loss of function; microwave electromagnetic radiation; mortality; neuroimaging; novel; reconstruction; tomography; virtual

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.

项目摘要 /摘要 根据疾病预防控制中心的说法，每40秒发生一次中风一次，每4分钟死亡一次 以及超过一半的65岁及以上的生存期间的流动性降低。能力 在院前环境中区分缺血性/出血性中风，并通过监测中风的演变 床边具有改善预后和降低死亡率的巨大潜力。不幸的是，最先进的 MRI和CT系统笨重且昂贵，将成像限制在临床环境和稀疏间隔内。 CT 还使用电离辐射，构成安全风险并进一步禁止经常想象。微波 层析成像（MTI）是MRI和CT的承诺替代/补充选择，但尚未是 在临床环境中使用。这主要是由于其空间分辨率差，因为特征维度为 与电子波的波长相当。不幸的是，降低波长（即 MTI的测量频率的增加是不可行的 组织内部的衰减。相反，我们的目标是探索扩大基本限制的可行性 MTI通过创新解决了使用低频测量估算高频数据的分辨率 深神经网络（DNNS）。我们靶向<1cm×1cm的中风的检测，满足临床期望 在院前环境以及整个中风监测过程中，急需的补充。假设 1：围绕生物成像测量的低频和高频数据之间存在关系 我们可以用来“人工”的​​域增加了任何给定低频的最高可用频率 测量。假设2：n时频率的“人造”增加将通过n提高图像分辨率 时间，无论使用的MTI重建方法如何。在这里，n取决于最高可用频率（to 可以确定），预计至少等于两个。该研究很重要，因为它揭示了 以前未知的知识来增强生物学培养基中的MTI分辨率。在AIM 1中，我们将开发 DNN使用2D/3D求解器，规范/解剖学头模型和新的一类入内辐射天线 具有前所未有的效率。我们的研究将验证假设1。在AIM 2中，我们将验证DNN 通过使用估计的高频数据来重建图像。我们的研究将验证 假设2。在AIM 3中，我们将使用组织发出的幻象实验验证DNN。成功的 重建将需要改善（较高）图像分辨率与最先进的MTI重建 相同的测量频率。使用实际与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效率。可行性将构成未来研究的基础 人类主题。我们设想这项技术是克服鞋面的急需的突破 用于各种诊断和/或院前评估应用的MTI算法的频率限制 Stoke应用程序及以后。