Direct 3D Reconstruction Methods for Electrical Impedance Tomography for Stroke Imaging

项目摘要 每四分钟就有一名美国人死于中风，相当于每年每19名美国人中就有一人死于中风。中风 是否将fi分类为缺血和出血。缺血性中风占所有中风的87%，是由脑血管阻塞引起的 导致大脑供血不足的血管(或动脉)。出血性中风发生在以下情况时 大脑泄漏或破裂，在大脑内部或周围释放过多的血液。不正确的Classifi阳离子可能会产生可怕的后果 如用抗凝剂(用于溶解血栓)治疗出血性中风(出血)患者 对于缺血性中风)可能是致命的。及早行动是最重要的，因为脑细胞缺乏的每一分钟 适当的血液fl会使更多的细胞死亡。目前的Classifi标记方法需要在医院进行测试，例如CT 或者对患者的大脑进行核磁共振扫描，导致治疗延迟。这些延误对病人的生存来说尤其漫长。 在农村社区。 电阻抗断层成像(EIT)是一种廉价、无电离的新兴医学成像手段 辐射，并使用无害的表面电流和电压测量(例如，打开)提供便携式高对比度图像 使用fl可伸缩帽子的头部)以恢复内部点状电属性(例如，大脑内部)。EIT可以恢复 电导率，衡量电流fl穿透材料的难易程度，以及介电常数，衡量材料的能力 储存电荷的材料。出血性中风对应于由于出血而导致的异常高电导率区域， 而缺血性中风表现为由于能量引起的细胞肿胀，导致电导率低于预期的区域 失败了。 拟议的项目解决了早期、快速、可移植的卒中fi分类与eIT的重要问题。一位批评者 EIT用于卒中成像的障碍一直是图像重建算法的敏感度不正确 领域建模和数据中的噪声。由于这些挑战，大多数研究都集中在监控应用程序上， 对于Classifi阳离子任务没有帮助。相比之下，这里提出的D-bar重建方法是唯一得到证实的 一种噪声和建模误差稳健的重建方法，该方法能够使用 非线性傅里叶域中的低通fi变换。D-bar方法已经在2D中获得了成功，但它们在 3D是发育不良的。本提案的重点是开发快速、健壮的基于D-bar的3D重建方法 部分边界问题，对处理行程EIT数据至关重要。将全面开发数值算法 以及三维部分边界问题，并在模拟和实验数据上进行了验证。先验信息，来自 解剖地图集将被嵌入到方法中，以提高分辨率和稳定性。随着低通fi的引入 在D-bar方法导致重建模糊的情况下，通过卷积神经网络进行后处理将提供 改善了图像质量。这项工作将是fi第一次为3DEIT数据开发稳健的计算算法，开放 中风成像的大门。

Project Summary Every four minutes an American dies from stroke, equating to approximately 1 in every 19 US deaths annually. Strokes are classified as ischemic and hemorrhagic. Ischemic strokes make up 87% of all strokes and are caused by a blockage in a blood vessel (or artery) resulting in a lack of blood to the brain. A hemorrhagic stroke occurs when an artery in the brain leaks or ruptures releasing excess blood in or around the brain. Incorrect classification can have dire consequences as treating a patient suffering from a hemorrhagic stroke (bleed) with anticoagulant drugs (used to dissolve blood clots for ischemic strokes) can prove fatal. Early action is of the utmost importance as each passing minute that brain cells lack the proper blood flow additional cells die. Current classification methods require tests performed at the hospital, e.g., CT or MRI scans of the patient's brain, leading to treatment delays. These delays are particularly lengthy for patients living in rural communities. Electrical Impedance Tomography (EIT) is an emerging medical imaging modality that is inexpensive, has no ionizing radiation, and provides portable high-contrast images using harmless surface current and voltage measurements (e.g., on the head using a flexible hat) to recover the internal point-wise electrical properties (e.g., inside the brain). EIT can recover conductivity, a measure of how easily current flows through a material, as well as permittivity, a measure of the ability of a material to store a charge. A hemorrhagic stroke corresponds to an area of abnormally high conductivity due to the bleed, whereas an ischemic stroke presents as an area of lower conductivity than expected due cellular swelling from energy failure. The proposed project addresses the important problem of early, fast, portable stroke classification with EIT. A critical barrier for the use of EIT for stroke imaging has been the sensitivity of the image reconstruction algorithms to incorrect domain modeling and noise in the data. Due to these challenges, most research has focused on monitoring applications, not helpful for the classification task. By contrast, the D-bar reconstruction method proposed here is the only proven noise and modeling error robust reconstruction method capable of recovering the true conductivity/permittivity using a low-pass filtering in a nonlinear Fourier domain. D-bar methods have been successful in 2D but their development in 3D is stunted. This proposal focuses on the development of fast, robust D-bar based reconstruction methods for the 3D partial boundary problem, critical to working with stroke EIT data. Numerical algorithms will be developed for the full and partial boundary problems in 3D and validated on simulated and experimental data. A priori information, from anatomical atlases, will be embedded into the methods for increased resolution and stability. As the low-pass filtering in D-bar methods leads to blurred reconstructions, post-processing through Convolutional Neural Networks will provide improved image quality. This work will be the first to develop robust computational algorithms for 3D EIT data, opening the door for stroke imaging.