DICOM for EIT

With EIT starting to be used in routine clinical practice [1], it important that the clinically relevant information is portable between hospital data management systems. DICOM formats are widely used clinically and cover many imaging modalities, though not specifically EIT. We describe how existing DICOM specifications, can be repurposed as an interim solution, and basis from which a consensus EIT DICOM ‘Supplement’ (an extension to the standard) can be writte

Estimation of thorax shape for forward modelling in lungs EIT

The thorax models for pre-term babies are developed based on the CT scans from new-borns and their effect on image reconstruction is evaluated in comparison with other available models

Rapid generation of subject-specific thorax forward models

For real-time monitoring of lung function using accurate patient geometry, shape information needs to be acquired and a forward model generated rapidly. This paper shows that warping a cylindrical model to an acquired shape results in meshes of acceptable mesh quality, in terms of stretch and aspect ratio

Torso shape detection to improve lung monitoring

Two methodologies are proposed to detect the patient-specific boundary of the chest, aiming to produce a more accurate forward model for EIT analysis. Thus, a passive resistive and an inertial prototypes were prepared to characterize and reconstruct the shape of multiple phantoms. Preliminary results show how the passive device generates a minimum scatter between the reconstructed image and the actual shap

Nanoparticle electrical impedance tomography

We have developed a new approach to imaging with electrical impedance tomography (EIT) using gold nanoparticles (AuNPs) to enhance impedance changes at targeted tissue sites. This is achieved using radio frequency (RF) to heat nanoparticles while applying EIT imaging. The initial results using 5-nm citrate coated AuNPs show that heating can enhance the impedance in a solution containing AuNPs due to the application of an RF field at 2.60 GHz

Discrete mathematical models for electrical impedance tomography

Electrical Impedance Tomography (EIT) is a non-invasive, portable and low-cost medical imaging technique. Different current patterns are injected to the surface of a conductive body and the corresponding voltages are measured also on the boundary. These mea-surements are the data used to infer the interior conductivity distribution of the object. However, it is well known that the reconstruction process is extremely ill-posed due to the low sensitivity of the boundary voltages to changes in the interior conductivity distribution. The reconstructed images also suffer from poor spatial resolution. In tomographic systems, the spatial resolution is related to the number of applied current patterns and to the number and positions of electrodes which are placed at the surface of the object under examination. Two mammographic sensors were recently developed at the University of Mainz in collaboration with Oxford Brookes University. These prototypes consist of a planar sensing head of circular geometry with twelve large outer (active) electrodes arranged on a ring of radius 4.4cm where the external currents are injected and a set of, respectively thirty six and fifty four point-like high-impedance inner (passive) electrodes arranged in a hexagonal pattern where the induced voltages are measured. Two 2D reconstruction methods were proposed for these devices, one based on resistor network models and another one which uses an integral equation formulation. The novelty of the device and hence of these imaging techniques consists exactly in the distinct use of active and passive electrodes. The 2D images of the conductivity distribution of the interior tissue of the breast provide only information about the existence and location of the tumour. In this thesis different circular designs for the sensing head of this EIT device were analysed. The 2D resistor network approach was adapted to the different data collection geometries and the sensitivity of the reconstructions with respect to errors in the simulate data were investigated before any modifications to the original design were made. A novel 3D reconstruction algorithm was also developed for a simpler geometry of the sensing head which consisted of a rectangular array of thirty six electrodes (twenty active+ sixteen passive). This electrode configuration as well as the proposed imaging technique are intended to be used for breast cancer detection. The algorithm is based on linearizing the conductivity about a constant value and allows real-time reconstructions. The perfor-mance of the algorithm was tested on numerically simulated data and small inclusions with conductivities three or four times the background lying beneath the data collection surface were successfully detected. The results were fairly stable with respect to the noise level in the data and displayed very good spatial resolution in the plane of electrodes

ELECTRICAL IMPEDANCE SPECTROSCOPY AND TOMOGRAPHY: APPLICATIONS ON PLANT CHARACTERIZATION

World population will grow to 9.6 billion by 2050 and global food production needs to be increased by 70% to feed the increased population. Hence, better insight into plant physiology can impart better quality in fruits, vegetables, and crops, and eventually contribute to food security and sustainability. In this direction, this thesis utilizes electrical sensing technology, electrical impedance spectroscopy (EIS) and tomography (EIT), for better understanding and characterization of a number of physiological and structural aspects of the plant. It investigates the dehydration process in onion and ripening process in avocado by EIS, and perform 3D structural imaging of root by EIT. The thesis tracks and analyzes the dynamics of natural dehydration in onion and also assesses its moisture content using EIS. The work develops an equivalent electrical circuit that simulates the response of the onion undergoing natural drying for a duration of three weeks. The developed electrical model shows better congruence with the experimental data when compared to other conventional models for plant tissue with a mean absolute error of 0.42% and root mean squared error of 0.55%. Moreover, the study attempts to find a correlation between the measured impedance data and the actual moisture content of the onions under test (measured by weighing) and develops a simple mathematical model. This model provides an alternative tool for assessing the moisture content of onion nondestructively. The model shows excellent correlation with the ground truth data with a deterministic coefficient of 0.977, root mean square error of 0.030 and sum of squared error of 0.013. Next, the thesis presents an approach that will integrate EIS and machine learning technique that allows us to monitor ripening degree of avocado. It is evident from this study that the impedance absolute magnitude of avocado gradually decreases as the ripening stages (firm, breaking, ripe and overripe) proceed at a particular frequency. In addition, Principal component analysis shows that impedance magnitude (two principal components combined explain 99.95% variation) has better discrimination capabilities for ripening degrees compared to impedance phase angle, impedance real part, and impedance imaginary part. The developed classifier utilizes two principal component features over 100 EIS responses and demonstrate classification over firm, breaking, ripe and overripe stages with an accuracy of 90%, precision of 93%, recall of 90%, f1-score of 90% and an area under ROC curve (AUC) of 88%. Later on, this thesis presents the design, development, and implementation of a low-cost EIT system and analyzes root imaging as well. The designed prototype consists of an electrode array system, an Impedance analyzer board, 2 multiplexer units, and an Arduino. The Eval-Ad5933-EBZ is used for measuring the bio-impedance of the root, and two CD74HC4067 Multiplexers are used as electrode switching unit. Measuring and data collecting are controlled by the Arduino, and data storage is performed in a PC. By performing Finite Element Analysis and solving forward and inverse problem, the tomographic image of the root is reconstructed. The system is able to localize and build 2D and 3D tomographic image of root in a liquid medium. This proposed low-cost and easy-to-access system enables the users to capture the repetitive, noninvasive and non-destructive image of a plant root. Furthermore, the study proposes a simple mathematical model, based on ridge regression, which can predict root biomass from EIT data nondestructively with an accuracy of more than 93%. Thus, this study offers plant scientists and crop consultants the ability to better understand plant physiology nondestructively and noninvasively