Introduction: Electrical Impedance Tomography (EIT) is an emerging clinical imaging technique. Functional EIT by Evoked Response (fEITER) was developed at the University of Manchester as a high-speed, functional brain imaging device for use at the bedside. This 32-electrode EIT system applies an injection frequency of 10kHz and captures data using a 10ms temporal resolution. This thesis reports on the first volunteer and patient trials undertaken using fEITER for the following conditions: (a) flashing visual sequence - 14 awake volunteers; (b) a voluntary Valsalva manoeuvre (VM) - 15 awake volunteers and (c) during the induction of anaesthesia - 16 elective surgical patients. Aims: The research presented in this thesis was undertaken to differentiate between noise and physiological changes in raw fEITER data signals. Methods: SNR was determined for fEITER. Raw fEITER signals were pre-processed to reduce noise and dominant trends before multiple comparisons between reference and stimulus data were undertaken. Histograms and ROC curves were produced to illustrate the difference between reference and stimulus fEITER data. AUC values for single-subject and pooled ROC curves were calculated to determine whether fEITER data can be reliably differentiated between reference and stimulus conditions. Approximate Entropy (ApEn) was applied to evaluate the regularity of high frequency components within fEITER data for each trial condition. Results: Average SNR values for fEITER acquired using mesh and physical phantoms ranged from 62.94dB to 63.58dB, and 28.29dB to 31.45dB respectively. The following AUC values were acquired: Visual stimulus-frontal electrode pairs and electrode pairs overlying the visual cortex 0.520 and 0.505 respectively; VM: 0.658; and induction of anaesthesia: 0.547. The VM induced the greatest difference between pooled reference and stimulus data. Visual stimulation and induction of anaesthesia data showed poor distinction between pooled reference and stimulus data, although some single subject data did show a significant response. No significant differences were acquired for the comparison of ApEn-reference and ApEn-stimulus data for all trial conditions using a Wilcoxon's signed ranks test (visual stimulus-frontal electrode pairs: upper p = 0.998, visual stimulus-electrode overlying the visual cortex: upper p = 0.980; the VM: upper p = 0.976, and induction of anaesthesia: p = 0.912). Discussion: Although single-subject and pooled fEITER data recorded during the VM produced the greatest differences between reference and stimulus measurements, stimuli such as visual flashes and induction of anaesthesia may not be large enough to induce quantifiable changes between reference and stimulus data recorded from single electrode pairs. Collectively, these results provide little evidence to show that pre-processing of raw fEITER data amplifies features in fEITER waveforms which may be representative of physiological changes induced by an applied stimulus.

Electrical Impedance Tomography (EIT) is a technique for imaging the spatial distribution of conductivity inside a body using the boundary voltages, in response to applied current patterns, to reconstruct an image. Even though EIT has been proved useful in several medical applications such as mechanical respiration and ventilation monitoring of the lungs, its reported success in localising cerebral conductivity changes due to brain stimulation is very scant. In the case of the human head, the amplitude of the brain response to stimulation is usually very small and gets contaminated with physiological noise initiated from inside the cranium or the scalp. Three types of evoked responses were experimentally investigated: auditory startle response (ASR), CO2 reactivity response, and transient hyperaemic response (THR). ASR is expected to be a result of the brain’s functioning processes. However, the responses to CO2 and THR are expected to be due to cerebral blood volume or flow, due to physiological intervention in blood supply. According to the results, even when the amplitude of EIT measurements shows profound variation as in the case of CO2 reactivation, those could not be physiologically linked to the targeted responses and have been shown to be initiated from the scalp. The consistency of the measurements in the case of CO2 reactivation response was poor (37.50-50%). Meanwhile in the case of THR, although the magnitude of conductivity changes was overall 50% smaller than the previous cases, the subject movement was not necessary. This could be a reason that the consistency of THR case was very good (87%), and this can emphasize the necessity to maintain the changes in the scalp at minimum levels. In the case of ASR the response magnitude was very small (six times smaller than the CO2 reactivity case), and the evoked response can be detected with only 50% consistency. To measure very small EIT signals (such as those expected due to brain function) effectively, one must improve the sensitivity of the measurements to conductivity changes by increasing the excitation current. The functional EIT for Evoked Response (fEITER) system used in our investigations was modified from its initial configuration to increase its excitation current from 1 mApk-pk to 2 mApk-pk or 1 mArms. The bit-truncation in the process of Phase-Sensitive Detection (PSD) has also been improved, to modify the original 16-bit data readout to be 24-bit data readout. These improvements have doubled the instrument’s sensitivity, and have substantially reduced the truncation error to about 183 times. The quality of the physiological waveform was also significantly improved. Therefore, one could study more effectively very fast brain response using the modified system. For example, the latency of responses can be more precisely extracted, or the monitoring of the conductivity change in a period of only a few tens of milliseconds is then possible. The reconstruction of brain images corresponding to these physiologically evoked responses has been the ultimate goal of this thesis. To ensure obtaining the correct images, some crucial issues regarding EIT reconstruction were firstly investigated. One of these issues concerns the modelling error of the numerical head models. The reconstruction requires an accurate model capturing the geometry of the subject’s head with electrodes attached and accurate in-vivo tissue conductivities. However, since it is usually impractical to have a personalised model for each subject, many different head models (including a subject model) were constructed and investigated, to evaluate the possibility of using a generic model for all subjects. The electrode geometry was also carefully included into the models to minimise error. Another issue concerns the appropriate reconstruction algorithm. A novel nonlinear reconstruction method, based on the difference imaging approach and Generalized Minimal Residual method (GMRes) algorithm, with optimal parameters and prior information, was proposed to deal with significant modelling errors. With this algorithm, the experimental results showed that it is possible to use a generic model for reconstructing an impedance change, but the magnitude of the change should be rather small. The last issue tackled was regarding the a priori choice of model parameters, and in particular the tissue conductivities. The tissue conductivities of the scalp and the skull were also estimated by a proposed methodology based on the Gauss-Newton method. The estimation showed that, compared to previous reported values, the conductivity of the scalp was higher, at 0.58 S/m, and that of the skull lower, at 0.008 S/m. Eventually, by exploiting the hardware and firmware advances in the measuring instrument in conjunction with the proposed modelling and reconstruction algorithm, processing our experimental EIT data captured on human heads and a head-like tank confirm that the localisation and imaging of conductivity changes occurring within the head is indeed possible. From the low quality measurements in the case of the CO2 reactivity response, the reconstructed images of this response do not reflect the true conductivity change. The consistency of the images to localise the sources of the changes was very poor (0-50%), i.e. the conductivity changing locations in the images were likely to be random. Our analysis suggests that the changes inside the cranium are likely to be due to the large change in the scalp. In the case of THR, the reconstructed images were able to localise the response in a similar manner to what had been found on the measurements, and the consistency was quite high (76%). Meanwhile, in the case of ASR, surprisingly the consistency of the images was 82%, much higher than the consistency of the measurements, which was only 50%. This was because the changing amplitude of the measurements was too small to be noticed by visualisation, and it was practically cumbersome to investigate all measurements. This statistic confirms that image reconstruction can reveal information that is not directly apparent by observing the measurements. In summary, EIT can be used in brain (function) imaging applications to some extent. The targeted response, which typically originates from inside the cranium is always infused with neurophysiological noise or physical noise at the scalp, and the amplitude of noise determines the possibility to localise the changes. It is also necessary for the desired response to have sufficiently large amplitude. These results show that EIT has been successful in THR and ASR, but for CO2 reactivity response, EIT lacks the necessary sensitivity.