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Cerebral Blood Flow Imaging based on 3D Electrical Impedance Tomography

基于3D电阻抗断层扫描的脑血流成像

基本信息

批准号：
EP/P006833/1
负责人：
Jiabin Jia
金额：
$ 12.86万
依托单位：
University of Edinburgh
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FP006833%2F1
关键词：
Cerebral Blood Flow Imaging based

项目摘要

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.

脑血流量（CBF）是反映脑内血流灌注状况的重要指标。脑血流量的改变往往与脑疾病和脑功能紊乱有关，如脑卒中。成像对于人脑研究至关重要。临床上各种神经影像设备都可以测量局部脑血流量，但其时间分辨率仅能达到秒级，不足以监测脑血流量的快速变化。此外，这些设备昂贵的运行成本限制了连续床边在线监测的可用性。本项目旨在开发一种新的方法，利用三维电阻抗断层成像（EIT）的CBF成像。由于血液的电导率与其他脑组织的电导率有显著的差异，EIT能够无创地产生具有2毫秒时间分辨率的脑内电导率分布的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，我们提出了以下三个目标：（1）探索脑组织丰富的光谱信息，选择最佳的EIT工作频率;（2）开发先进的图像重建算法，提高图像分辨率;（3）从序列EIT图像中计算脑血流的三维速度场。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）使用体素到体素互相关算法的CBF的3D速度场;（4）在真实头形体模上验证系统性能。所提出的方法可能用于诊断脑部疾病（例如中风，癫痫，脑肿瘤），监测大脑活动（例如创伤性脑损伤中脑灌注的非侵入性测量），并更多地了解人类认知过程（例如增加对痴呆症的理解和早期识别）。最终，这项研究将导致对大脑疾病和大脑功能的新见解。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Bayesian Image Reconstruction Using Weighted Laplace Prior for Lung Respiratory Monitoring With Electrical Impedance Tomography

DOI：
10.1109/tim.2022.3220279
发表时间：
2023
期刊：
IEEE Transactions on Instrumentation and Measurement
影响因子：
5.6
作者：
Yang Wu;Bai Chen;Kai Liu;Shan Huang;Yan Li;J. Jia;Jiafeng Yao
通讯作者：
Yang Wu;Bai Chen;Kai Liu;Shan Huang;Yan Li;J. Jia;Jiafeng Yao

Accelerated Structure-Aware Sparse Bayesian Learning for Three-Dimensional Electrical Impedance Tomography

DOI：
10.1109/tii.2019.2895469
发表时间：
2019-09-01
期刊：
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
影响因子：
12.3
作者：
Liu, Shengheng;Wu, Hancong;Jia, Jiabin
通讯作者：
Jia, Jiabin

Shape Reconstruction With Multiphase Conductivity for Electrical Impedance Tomography Using Improved Convolutional Neural Network Method

DOI：
10.1109/jsen.2021.3050845
发表时间：
2021-04
期刊：
IEEE Sensors Journal
影响因子：
4.3
作者：
Yang Wu;Bai Chen;Kai Liu;Chengjun Zhu;Huaping Pan;J. Jia;Hongtao Wu;Jiafeng Yao
通讯作者：
Yang Wu;Bai Chen;Kai Liu;Chengjun Zhu;Huaping Pan;J. Jia;Hongtao Wu;Jiafeng Yao

Time Sequence Learning for Electrical Impedance Tomography Using Bayesian Spatiotemporal Priors