Multi-electrode stimulation and measurement patterns versus prior information of fast 3D EIT

Electrical Impedance Tomography or as referred to as EIT, is a typical inverse problem of estimating the unknown interior material impedance properties inside a conductive medium through measurements performed at the periphery of the containing medium. Due to its inverse nature, EIT’s poor spatial resolution is still one of its biggest downfalls since meaningful images are hard to obtain without incorporating some sort of prior information about the material distribution characteristics. Given the ill-posedness of the EIT problem coupled with the limited number of collectable boundary voltage measurements, the resulted discrete system is heavily underdetermined and ill-conditioned. Therefore, a sensible step to overcome this problem is to collect as many measurements as the number of the finite elements composing the medium. From one hand, this is not practically possible, on the other, an increased number of measurements will contribute towards unrealistically high computational overheads both for the assembly and the inversion of the resulted dense system matrix. For any given EIT configuration, the discrete Picard’s stability criterion can be deployed as a practical measure of the system performance against noise contaminated measurements. Herein, this study includes extensive use of this measure to quantify the performance of impedance imaging systems for various injection patterns. In effect, it is numerically demonstrated that by varying electrode distributions and numbers, little improvement, if any, in the performance of the impedance imaging system is recorded. In contrast, by using groups of electrodes in the 3D current injection process, a step increase in performance is obtained. Numerical results reveal that the performance measure of the imaging system is 29% for a conventional combination of stimulation and prior information, 97% for groups of electrodes and the same prior and 98% for groups of electrodes and a more accurate prior. Finally, since a smaller number of electrodes are involved in the measurement process, a smaller number of measurements are acquired. However, no compromise in the quality of the reconstructed images is observed

Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion

Neurosurgery is a demanding medical discipline that requires a complex interplay of several neuroimaging techniques. This allows structural as well as functional information to be recovered and then visualized to the surgeon. In the case of tumor resections this approach allows more fine-grained differentiation of healthy and pathological tissue which positively influences the postoperative outcome as well as the patient's quality of life. In this work, we will discuss several approaches to establish thermal imaging as a novel neuroimaging technique to primarily visualize neural activity and perfusion state in case of ischaemic stroke. Both applications require novel methods for data-preprocessing, visualization, pattern recognition as well as regression analysis of intraoperative thermal imaging. Online multimodal integration of preoperative and intraoperative data is accomplished by a 2D-3D image registration and image fusion framework with an average accuracy of 2.46 mm. In navigated surgeries, the proposed framework generally provides all necessary tools to project intraoperative 2D imaging data onto preoperative 3D volumetric datasets like 3D MR or CT imaging. Additionally, a fast machine learning framework for the recognition of cortical NaCl rinsings will be discussed throughout this thesis. Hereby, the standardized quantification of tissue perfusion by means of an approximated heating model can be achieved. Classifying the parameters of these models yields a map of connected areas, for which we have shown that these areas correlate with the demarcation caused by an ischaemic stroke segmented in postoperative CT datasets. Finally, a semiparametric regression model has been developed for intraoperative neural activity monitoring of the somatosensory cortex by somatosensory evoked potentials. These results were correlated with neural activity of optical imaging. We found that thermal imaging yields comparable results, yet doesn't share the limitations of optical imaging. In this thesis we would like to emphasize that thermal imaging depicts a novel and valid tool for both intraoperative functional and structural neuroimaging

Advanced digital electrical impedance tomography system for biomedical imaging

Electrical Impedance Tomography (EIT) images the spatial conductivity distribution in an electrode-bounded sensing domain by non-intrusively generating an electric field and measuring the induced boundary voltage. Since its emergence, it has attracted ample interest in the field of biomedical imaging owing to its fast, cost efficient, label-free and non-intrusive sensing ability. Well-investigated biomedical applications of the EIT include lung ventilation monitoring, breast cancer imaging, and brain function imaging. This thesis probes an emerging biomedical application of EIT in three dimensional (3D) cell culture imaging to study non-destructively the biological behaviour of a 3D cell culture system, on which occasion real-time qualitative and quantitative imaging are becoming increasingly desirable. Focused on this topic, the contribution of the thesis can be summarised from the perspectives of biomedical-designed EIT system, fast and effective image reconstruction algorithms, miniature EIT sensors and experimental studies on cell imaging and cell-drug response monitoring, as follows. First of all, in order to facilitate fast, broadband and real-time 3D conductivity imaging for biomedical applications, the design and evaluation of a novel multi-frequency EIT (mfEIT) system was presented. The system integrated 32 electrode interfaces and its working frequency ranged from 10 kHz to 1 MHz. Novel features of the system included: a) a fully adjustable multi-frequency current source with current monitoring function was designed; b) a flexible switching scheme together with a semi-parallel data acquisition architecture was developed for high-frame-rate data acquisition; c) multi-frequency simultaneous digital quadrature demodulation was accomplished, and d) a 3D imaging software, i.e. Visual Tomography, was developed to perform real-time two dimensional (2D) and 3D image reconstruction, visualisation and analysis. The mfEIT system was systematically tested and evaluated on the basis of the Signal to Noise Ratio (SNR), frame rate, and 2D and 3D multi-frequency phantom imaging. The highest SNR achieved by the system was 82.82 dB on a 16-electrode EIT sensor. The frame rate was up to 546 frames per second (fps) at serial mode and 1014 fps at semi-parallel mode. The evaluation results indicate that the presented mfEIT system is a powerful tool for real-time 2D and 3D biomedical imaging. The quality of tomographic images is of great significance for performing qualitative or quantitative analysis in biomedical applications. To realise high quality conductivity imaging, two novel image reconstruction algorithms using adaptive group sparsity constraint were proposed. The proposed algorithms considered both the underlying structure of the conductivity distribution and sparsity priors in order to reduce the degree of freedom and pursue solutions with the group sparsity structure. The global characteristic of inclusion boundaries was studied as well by imposing the total variation constraint on the whole image. In addition, two adaptive pixel grouping methods were also presented to extract the structure information without requiring any a priori knowledge. The proposed algorithms were evaluated comparatively through numerical simulation and phantom experiments. Compared with the state-of-the-art algorithms such as l1 regularisation, the proposed algorithms demonstrated superior spatial resolution and preferable noise reduction performance in the reconstructed images. These features were demanded urgently in biomedical imaging. Further, a planar miniature EIT sensor amenable to the standard 3D cell culture format was designed and a 3D forward model was developed for 3D imaging. A novel 3D-Laplacian and sparsity joint regularisation algorithm was proposed for enhanced 3D image reconstruction. Simulated phantoms with spheres located at different vertical and horizontal positions were imaged for 3D imaging performance evaluation. Image reconstructions of MCF-7 human breast cancer cell spheroids and triangular breast cancer cell pellets were carried out for experimental verification. The results confirmed that robust impedance measurement on the highly conductive cell culture medium was feasible and, greatly improved image quality was obtained by using the proposed regularisation method. Finally, a series of cancer cell spheroid imaging tests and real-time cell-drug response monitoring experiments by using the developed mfEIT system (Chapter 3), the designed miniature EIT sensors (Chapter 6) and the proposed image reconstruction algorithms (Chapter 4, 5 and 6) were carried out followed by comparative analysis. The stability of long-term impedance measurement on the highly conductive cell culture medium was verified firstly. Subsequently, by using the proposed algorithms in Chapter 4 and Chapter 5, high quality cancer cell spheroid imaging on a miniature sensor with 2D electrode configuration was achieved. Further, preliminary experiments on real-time monitoring of human breast cancer cell and anti-cancer drug response were performed and analysed. Promising results were obtained from these experiments. In summary, the work demonstrated in this thesis validated the feasibility of using the developed mfEIT system, the proposed image reconstruction algorithms, as well as the designed miniature EIT sensors to visualise 3D cell culture systems such as cell spheroids or artificial tissues and organs. The established work would expedite the real-time qualitative and quantitative imaging of 3D cell culture systems for the rapid assessment of cellular dynamics

Multimodal Characterization of the Atrial Substrate - Risks and Rewards of Electrogram and Impedance Mapping

The treatment of atrial rhythm disorders such as atrial fibrillation has remained a major challenge predominantly for patients with severely remodeled substrate. Individualized ablation strategies beyond pulmonary vein isolation in combination with real-time assess- ment of ablation lesion formation have been striven for insistently. Current approaches for identifying arrhythmogenic regions predominantly rely on electrogram-based features such as activation time and voltage or electrogram fractionation as a surrogate for tissue pathology. Despite bending every effort, large-scale clinical trials have yielded ambiguous results on the efficacy of various substrate mapping approaches without significant improvement of patient outcomes. This work focuses on enhancing the understanding of electrogram features and local impedance measurements in the atria towards the extraction of clinically relevant and predic- tive substrate characteristics. Features were extracted from intra-atrial electrograms with particular reference to the un- derlying excitation patterns to address morphological alterations caused by structural and functional changes. The noise level of unipolar electrograms was estimated and reduced by tailored filtering to enhance unipolar signal quality. Electrogram features exhibited nar- row distributions for healthy substrate across patients while a wide range was observed for pathologically altered excitation. Additionally, local impedance was investigated as a novel parameter and mapping modality. Having been introduced to the medical device market recently for monitoring ablative lesion formation, initial clinical experiences with local impedance-enabled catheters lack comple- mentary systematic investigations. Confounding factors and the potential for application as a tool for substrate mapping need elucidation. This work pursued a trimodal approach combining in human, in vitro, and in silico experiments to quantitatively understand the effect of distinct ambient conditions on the measured local impedance. Forward simulations of the spread of the electrical field with a finite element approach as well as the application of inverse solution methods to reconstruct tissue conductivity were implemented in silico. Adequate preprocessing steps were developed for measurements in human to eliminate artefacts automatically. Two clinical studies on local impedance as an indicator for ablation lesion formation and on local impedance based substrate mapping were conducted. Local impedance recordings identified both previously ablated and native scar areas irrespective of local excitation. A highly detailed in silico environment for local impedance measurements was validated with in vitro recordings and provided quantitative insights into the influence of changes in clinically relevant scenarios. Inverse reconstruction of relative tissue conductivity yielded promising results in silico. This work demonstrates that local impedance mapping shows great potential to comple- ment electrogram-based substrate mapping. A validated in silico environment for local impedance measurements can facilitate and optimize the development of next generation local impedance-enabled catheters. Conduction velocity, electrogram features, and recon- structed tissue conductivity suggest to be promising candidates for enhancing future clinical mapping systems

Regularisation methods for imaging from electrical measurements

In Electrical Impedance Tomography the conductivity of an object is estimated from boundary measurements. An array of electrodes is attached to the surface of the object and current stimuli are applied via these electrodes. The resulting voltages are measured. The process of estimating the conductivity as a function of space inside the object from voltage measurements at the surface is called reconstruction. Mathematically the ElT reconstruction is a non linear inverse problem, the stable solution of which requires regularisation methods. Most common regularisation methods impose that the reconstructed image should be smooth. Such methods confer stability to the reconstruction process, but limit the capability of describing sharp variations in the sought parameter. In this thesis two new methods of regularisation are proposed. The first method, Gallssian anisotropic regularisation, enhances the reconstruction of sharp conductivity changes occurring at the interface between a contrasting object and the background. As such changes are step changes, reconstruction with traditional smoothing regularisation techniques is unsatisfactory. The Gaussian anisotropic filtering works by incorporating prior structural information. The approximate knowledge of the shapes of contrasts allows us to relax the smoothness in the direction normal to the expected boundary. The construction of Gaussian regularisation filters that express such directional properties on the basis of the structural information is discussed, and the results of numerical experiments are analysed. The method gives good results when the actual conductivity distribution is in accordance with the prior information. When the conductivity distribution violates the prior information the method is still capable of properly locating the regions of contrast. The second part of the thesis is concerned with regularisation via the total variation functional. This functional allows the reconstruction of discontinuous parameters. The properties of the functional are briefly introduced, and an application in inverse problems in image denoising is shown. As the functional is non-differentiable, numerical difficulties are encountered in its use. The aim is therefore to propose an efficient numerical implementation for application in ElT. Several well known optimisation methods arc analysed, as possible candidates, by theoretical considerations and by numerical experiments. Such methods are shown to be inefficient. The application of recent optimisation methods called primal- dual interior point methods is analysed be theoretical considerations and by numerical experiments, and an efficient and stable algorithm is developed. Numerical experiments demonstrate the capability of the algorithm in reconstructing sharp conductivity profiles

Preliminary studies in imaging neuronal depolarization in the brain with electrical or magnetic detection impedance tomography.

Electrical impedance Tomography (EIT) is a novel medical imaging method which has the potential to provide the revolutionary advance of a method to image fast neural activity non-invasively. by imaging electrical impedance changes over milliseconds which occur when neuronal ion channels open during activity. These changes have been estimated to be c.1% locally in cerebral cortex, if measured with applied current below 100Hz. The purpose of this work was to determine if such changes could be reproducibly recorded in humans non invasive First, a novel recessed electrode was designed and tested to determine to enable a maximal current of 1mA to be applied to the scalp without causing painful skin sensation. Modelling indicated that this produced a peak current density of 0.3A/m2 in underlying cortex, which was below the threshold for stimulation. Next, the signal-to-noise ratio of impedance changes during evoked visual activity was investigated in healthy volunteers with current injected with scalp electrodes and recording of potential by scalp electrodes (Low Frequency EIT) or magnetic field by magnetoencephalography (Magnetic Detection EIT). Numerical FEM simulations predicted that resistivity changes of 1% in the primary7 visual cortex translate into scalp voltage changes of IjiV (0.004%) and external magnetic field changes of 30fT (0.2%) and were independently validated in saline filled tanks. In vivo, similar changes with a signal-to-noise ratio of 3 after averaging for 10 minutes were recorded for both methods the main noise sources were background brain activity and the current source. These studies with non-invasive scalp recording have, for the first time, demonstrated the existence of such changes when measured non-invasively. These are unfortunately too low to enable reliable imaging within a realistic recording time but support the view that such imaging could be possible in animal or human epileptic studies with electrodes placed on the brain or non-invasively following technological improvements this further work is currently in progress

Multi-frequency segmental bio-impedance device:design, development and applications

Bio-impedance analysis (BIA) provides a rapid, non-invasive technique for body composition estimation. BIA offers a convenient alternative to standard techniques such as MRI, CT scan or DEXA scan for selected types of body composition analysis. The accuracy of BIA is limited because it is an indirect method of composition analysis. It relies on linear relationships between measured impedance and morphological parameters such as height and weight to derive estimates. To overcome these underlying limitations of BIA, a multi-frequency segmental bio-impedance device was constructed through a series of iterative enhancements and improvements of existing BIA instrumentation. Key features of the design included an easy to construct current-source and compact PCB design. The final device was trialled with 22 human volunteers and measured impedance was compared against body composition estimates obtained by DEXA scan. This enabled the development of newer techniques to make BIA predictions. To add a ‘visual aspect’ to BIA, volunteers were scanned in 3D using an inexpensive scattered light gadget (Xbox Kinect controller) and 3D volumes of their limbs were compared with BIA measurements to further improve BIA predictions. A three-stage digital filtering scheme was also implemented to enable extraction of heart-rate data from recorded bio-electrical signals. Additionally modifications have been introduced to measure change in bio-impedance with motion, this could be adapted to further improve accuracy and veracity for limb composition analysis. The findings in this thesis aim to give new direction to the prediction of body composition using BIA. The design development and refinement applied to BIA in this research programme suggest new opportunities to enhance the accuracy and clinical utility of BIA for the prediction of body composition analysis. In particular, the use of bio-impedance to predict limb volumes which would provide an additional metric for body composition measurement and help distinguish between fat and muscle content