Cerebral Blood Flow Imaging based on 3D Electrical Impedance Tomography

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Engineering


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.

Planned Impact

The proposed research fits the strategic EPSRC theme of healthcare technologies. Optimizing treatment is one of the grand challenges of this theme. It focuses on the technologies facilitating effective diagnosis, and patient-specific prediction to provide evidence for early intervention.

In the same way that echocardiography and electrocardiography have innovated the care of people with heart problems, the technique being proposed could do similar for people with neurological diseases. Based on impedance measurement, EIT is a low-cost, portable and fast medical imaging technique which could be used to measure cerebral blood flow and hence has diagnostic, monitoring and therapeutic utilities in conditions involving the brain.

The work proposed will provide a new insight into cerebral hemodynamics and maintain UK's academic strengths in tomographic medical imaging, modelling and sensor technology. A high-quality, affordable and portable imaging tool for the brain and blood vessels is a key part of a successful prognosis of cerebrovascular diseases, such as stroke. According to stroke statistics published in January 2016, stroke is the second single most common cause of death in the world causing 6.7 million deaths each year. Stroke is one of the largest causes of disability and half of all stroke survivors have a disability. Stroke occurs approximately 152,000 times a year in the UK. The economic costs of stroke in the UK from a societal perspective totals around £9 billion a year. It is critically important from societal and economic points of view that stroke patients receive early diagnosis and assessment to avoid or minimize permanent brain damage leading to permanent disability and lifetime dependency on NHS support and reduce healthcare costs. EIT can help to distinguish the stroke types (ischemic stroke or haemorrhage stroke) and to assist stroke researchers to understand the effectiveness of anti-stroke drugs, improve treatment techniques, and to investigate new reperfusion strategies.

Epilepsy is a condition affecting the brain and causing repeated seizures. It is estimated that epilepsy affects more than 500,000 people in the UK. About one third of the patients do not respond to anti-epileptic drugs, and removing the specific area of the brain where epileptic activity starts (the epileptogenic zone) is a very common option to treat epilepsy. Therefore, high-quality EIT images can benefit accurate localization of the epileptogenic source, which is the imperative step before the surgery.

Due to its excellent temporal resolution, the proposed EIT brain imaging is able to produce supplementary information to disclose the functional regions and connectivity network pattern related to specific stimuli, which enables research into verifying the functional area of the brain, and assessing the effects of the degenerative diseases, such as Alzheimer's disease.


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Liu S (2020) Time Sequence Learning for Electrical Impedance Tomography Using Bayesian Spatiotemporal Priors in IEEE Transactions on Instrumentation and Measurement

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Wang Q (2022) Error-Constraint Deep Learning Scheme for Electrical Impedance Tomography (EIT) in IEEE Transactions on Instrumentation and Measurement

Description A number of new imaging reconstruction methods are developed for electrical impedance tomography in this project.
Exploitation Route Research papers generated from this project were cited by many other research groups in the world. DOI: 10.1109/TMI.2018.2816739 publised in 2018 were cited 32 times by Feb 2020. DOI: 10.1109/TII.2019.2895469 publised in 2019 were citied 16 time by Feb 2020.
Sectors Digital/Communication/Information Technologies (including Software),Healthcare