Cerebral Blood Flow Imaging based on 3D Electrical Impedance Tomography

脑血流量(CBF)是反映脑血流灌注状态的重要标志。脑血流量的变化总是与脑部疾病和障碍有关，例如中风。成像对人脑研究至关重要。各种临床神经影像设备可以测量局部脑血流量，但其时间分辨率只能达到秒级，不足以快速监测脑血流量的快速变化。此外，昂贵的操作成本限制了床边连续在线监测的可用性。本项目旨在开发一种使用三维电阻抗断层成像(EIT)进行脑血流成像的新方法。由于血液的电导率与其他脑组织的电导率有明显的差异，EIT能够以2毫秒的时间分辨率非侵入性地产生脑组织电导率分布的3D图像。已经进行了开创性的研究，结果表明EIT是一种很有前途的脑成像技术。为了提高EIT的性能，提出了以下三个目标：(1)挖掘脑组织丰富的光谱信息，选择EIT的最佳工作频率；(2)开发先进的图像重建算法，以提高图像分辨率；(3)从EIT序列图像计算CBF的三维速度场。这些目标将在四个工作包中实现：(1)基于Chirp信号和小波变换的宽带多频EIT；(2)使用稀疏约束作为先验信息的3D脑图像重建；(3)基于体素到体素互相关算法的CBF三维速度场；(4)在真实头形体模上的系统性能验证。该方法可能用于诊断脑部疾病(如中风、癫痫、脑肿瘤)，监测脑活动(如无创测量创伤性脑损伤的脑血流灌注)，并了解更多关于人类认知过程的信息(如增加对痴呆症的理解和早期识别)。最终，这项研究将带来对大脑疾病和大脑功能的新见解。

Cerebral Blood Flow (CBF) is an importance marker to indicate the status of blood perfusion in the brain. The change of CBF is always associated with brain disease and disorder, for instance, stroke. Imaging is critical for human brain research. Various clinical neuroimaging equipment allows measurement of regional CBF, however, their temporal resolution can only reach the scale of seconds, which is not fast enough to monitor the rapid change of CBF. In addition, expensive operation cost of these equipment limits the availability of continuous bedside online monitoring.This project aims to develop a novel approach of imaging CBF using 3D Electrical Impedance Tomography (EIT). Since the electrical conductivity of blood has the distinctive difference with that of other brain tissue, EIT is able to noninvasively produce the 3D images of the electrical conductivity distribution in the brain with 2-millisecond temporal resolution. Pioneering research has been carried out and the results demonstrated EIT was a promising technique for brain imaging. Following three objectives are proposed to improve EIT's performance: (1) To explore the rich spectroscopic information of brain tissue and select optimal working frequency for EIT; (2) To develop advanced image reconstruction algorithm to improve image resolution; (3) To compute 3D velocity field of CBF from series EIT images. These objectives will be implemented in four work packages: (1) Wideband multifrequency EIT based on Chirp signal and wavelet transform; (2) 3D brain image reconstructions using sparsity constraint as prior information; (3) 3D Velocity field of CBF using voxel-to-voxels cross-correlation algorithm; (4) Validation of system performance on realistic head-shaped phantom. The proposed method could potentially be used to diagnose brain diseases (e.g. stroke, epilepsy, brain tumours), monitor cerebral activities (e.g. Non-invasive measurement of cerebral perfusion in traumatic brain injury), and learn more about the human cognitive process (e.g. increased understanding and early identification of dementia). Ultimately this research will lead to new insights into brain diseases and brain function.