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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要据 CDC 统计，美国每 40 秒就会发生一次中风，每 4 分钟就有一人死亡超过一半的 65 岁及以上幸存者的活动能力随之下降。在院前环境中区分缺血性/出血性中风，并通过以下方式监测中风演变：不幸的是，床旁治疗具有改善预后和降低死亡率的巨大潜力。 MRI 和 CT 系统体积庞大且价格昂贵，限制了临床环境和稀疏间隔的成像。还使用带来安全风险的电离辐射，并进一步禁止频繁成像。断层扫描成像 (MTI) 是 MRI 和 CT 的一种有前途的替代/补充选择，但尚未得到广泛应用。这主要是由于其空间分辨率较差，因为特征尺寸较小。不幸的是，波长缩短了（即，增加测量频率）MTI 是不可行的，因为高频容易产生噪声并且严重影响相反，我们的目标是探索扩大组织内部衰减的可行性。通过创新从低频测量中估计高频数据来实现 MTI 分辨率我们的目标是检测<1cm×1cm的中风，满足临床预期的深度神经网络（DNN）。院前环境和整个中风监测过程中急需的补充假设。 1：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使用该域“人为”增加任何给定低频的最高可用频率假设 2：“人为”将频率增加 N 倍将使图像分辨率提高 N 倍。次，无论使用何种 MTI 重建方法。这里，N 取决于最高可用频率（至）。待确定）并且预计至少等于二。这项研究很重要，因为它揭示了这一点。在目标 1 中，我们将开发增强生物介质中 MTI 分辨率的先前未知知识。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 我们的研究将以前所未有的效率验证假设 1。在目标 2 中，我们将验证 DNN。我们的研究将通过使用估计的高频数据重建图像来进行数值验证。假设 2。在目标 3 中，我们将使用组织模拟模型成功验证 DNN。与最先进的 MTI 重建相比，重建将需要提高（N 倍）图像分辨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可行性，并将成为未来研究的基础。我们认为这项技术是克服上层障碍所急需的突破。用于脑部各种诊断和/或院前评估应用的 MTI 算法的频率限制斯托克应用及其他。