Simplifying the hardware requirements for fast neural EIT of peripheral nerves

OBJECTIVE: The main objective of this study was to assess the feasibility of lowering the hardware requirements for fast neural EIT in order to support the distribution of this technique. Specifically, the feasibility of replacing the commercial modules present in the existing high-end setup with compact and cheap customized circuitry was assessed. APPROACH: Nerve EIT imaging was performed on rat sciatic nerves with both our standard ScouseTom setup and a customized version in which commercial benchtop current sources were replaced by custom circuitry. Electrophysiological data and images collected in the same experimental conditions with the two setups were compared. Data from the customized setup was subject to a down-sampling analysis to simulate the use of a recording module with lower specifications. MAIN RESULTS: Compound action potentials (573±287µV and 487±279µV, p=0.28) and impedance changes (36±14µV and 31±16µV, p=0.49) did not differ significantly when measured using commercial high-end current sources or our custom circuitry, respectively. Images reconstructed from both setups showed neglibile (<1voxel, i.e. 40µm) difference in peak location and a high degree of correlation (R2=0.97). When down-sampling from 24 to 16 bits ADC resolution and from 100KHz to 50KHz sampling frequency, signal-to-noise ratio showed acceptable decrease (<-20%), and no meaningful image quality loss was detected (peak location difference <1voxel, pixel-by-pixel correlation R2=0.99). SIGNIFICANCE: The technology developed for this study greatly reduces the cost and size of a fast neural EIT setup without impacting quality and thus promotes the adoption of this technique by the neuroscience research community

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

A 16-Channel Electrical Impedance Tomography System Using the Red Pitaya

Electrical Impedance Tomography (EIT) seeks to provide a new modality by which to image portions of the body and tissues which provide differences in conductivity, depending on their state. Unlike bulky, expensive, and hard-to-access traditional medical imaging equipment, such as those used in magnetic resonance imaging and computed tomography, EIT is potentially capable of being contained to small, portable, inexpensive hardware. Here, a Red Pitaya (RP) device is used to provide and process signals that can be generated and multiplexed into 16 channels, all while data is gathered from a set of electrodes embedded into a tank containing electrically conductive simulated tissue. A voltage-controlled current source provides a known value of current to be introduced with each new set of measured signals, which are then amplified and filtered to preprocess the signals. The data is then gathered together by the RP and communicated to a MATLAB program running on a nearby PC through a Standard Commands for Programmable Instrumentation interface, where the data is reconstructed using regularization with EIDORS, a MATLAB toolkit. Precision and accuracy were measured by evaluating the signal-to-noise-ratio (SNR) and a 2D Pearson correlation coefficient to the ideal images of the tank. The first tank design used electrodes of a small, pointed design, while the second used 1 cm2 copper plates for electrodes. The first had a mean system SNR of 31.616 dB (11.347) before filtering and 34.176dB (11.9803) after, and a correlation coefficient of r = 0.702 (0.055), while the second tank showed negative results

Electrical Impedance Tomography/Spectroscopy (EITS): a Code Division Multiplexed (CDM) approach

Electrical Impedance Tomography and Spectroscopy (EITS) is a noninvasive imaging technique that creates images of cross-sections "tomos" of objects by discriminating them based on their electrical impedance. This thesis investigated and successfully confirmed the use of Code Division Multiplexing (CDM) using Gold codes in Electrical Impedance Tomography and Spectroscopy. The results obtained showed 3.5% and 6.2% errors in determining the position and size of imaged anomalies respectively, with attainable imaging speed of 462 frames/second. These results are better, compared to those reported when using Time Division Multiplexing (TDM) and Frequency Division Multiplexing (FDM).This new approach provides a more robust mode of EITS for fast changing dynamic systems by eliminating temporal data inconsistencies. Furthermore, it enables robust use of frequency difference imaging and spectroscopy in EITS by eliminating frequency data inconsistencies. In this method of imaging, electric current patterns are safely injected into the imaged object by a set of electrodes arranged in a single plane on the objects surface, for 2-Dimensional (2D) imaging. For 3-Dimensional (3D) imaging, more electrode planes are used on the objects surface. The injected currents result in measurable voltages on the objects surface. Such voltages are measured, and together with the input currents, and a Finite Element Model (FEM) of the object, used to reconstruct an impedance image of the cross-sectional contents of the imaged object. The reconstruction process involves the numerical solutions of the forward problem; using Finite Element solvers and the resulting ill-posed inverse problem using iterative Optimization or Computational Intelligence methods. This method has applications mainly in the Biomedical imaging and Process monitoring fields. The primary interests of the author are, in imaging and diagnosis of cancer, neonatal pneumonia and neurological disorders which are leading causes of death in Africa and world-wide

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

Advances in Integrated Circuits and Systems for Wearable Biomedical Electrical Impedance Tomography

Electrical impedance tomography (EIT) is an impedance mapping technique that can be used to image the inner impedance distribution of the subject under test. It is non-invasive, inexpensive and radiation-free, while at the same time it can facilitate long-term and real-time dynamic monitoring. Thus, EIT lends itself particularly well to the development of a bio-signal monitoring/imaging system in the form of wearable technology. This work focuses on EIT system hardware advancement using complementary metal oxide semiconductor (CMOS) technology. It presents the design and testing of application specific integrated circuit (ASIC) and their successful use in two bio-medical applications, namely, neonatal lung function monitoring and human-machine interface (HMI) for prosthetic hand control. Each year fifteen million babies are born prematurely, and up to 30% suffer from lung disease. Although respiratory support, especially mechanical ventilation, can improve their survival, it also can cause injury to their vulnerable lungs resulting in severe and chronic pulmonary morbidity lasting into adulthood, thus an integrated wearable EIT system for neonatal lung function monitoring is urgently needed. In this work, two wearable belt systems are presented. The first belt features a miniaturized active electrode module built around an analog front-end ASIC which is fabricated with 0.35-µm high-voltage process technology with ±9 V power supplies and occupies a total die area of 3.9 mm². The ASIC offers a high power active current driver capable of up to 6 mAp-p output, and wideband active buffer for EIT recording as well as contact impedance monitoring. The belt has a bandwidth of 500 kHz, and an image frame rate of 107 frame/s. To further improve the system, the active electrode module is integrated into one ASIC. It contains a fully differential current driver, a current feedback instrumentation amplifier (IA), a digital controller and multiplexors with a total die area of 9.6 mm². Compared to the conventional active electrode architecture employed in the first EIT belt, the second belt features a new architecture. It allows programmable flexible electrode current drive and voltage sense patterns under simple digital control. It has intimate connections to the electrodes for the current drive and to the IA for direct differential voltage measurement providing superior common-mode rejection ratio (CMRR) up to 74 dB, and with active gain, the noise level can be reduced by a factor of √3 using the adjacent scan. The second belt has a wider operating bandwidth of 1 MHz and multi-frequency operation. The image frame rate is 122 frame/s, the fastest wearable EIT reported to date. It measures impedance with 98% accuracy and has less than 0.5 Ω and 1° variation across all channels. In addition the ASIC facilitates several other functionalities to provide supplementary clinical information at the bedside. With the advancement of technology and the ever-increasing fusion of computer and machine into daily life, a seamless HMI system that can recognize hand gestures and motions and allow the control of robotic machines or prostheses to perform dexterous tasks, is a target of research. Originally developed as an imaging technique, EIT can be used with a machine learning technique to track bones and muscles movement towards understanding the human user’s intentions and ultimately controlling prosthetic hand applications. For this application, an analog front-end ASIC is designed using 0.35-µm standard process technology with ±1.65 V power supplies. It comprises a current driver capable of differential drive and a low noise (9μVrms) IA with a CMRR of 80 dB. The function modules occupy an area of 0.07 mm². Using the ASIC, a complete HMI system based on the EIT principle for hand prosthesis control has been presented, and the user’s forearm inner bio-impedance redistribution is assessed. Using artificial neural networks, bio-impedance redistribution can be learned so as to recognise the user’s intention in real-time for prosthesis operation. In this work, eleven hand motions are designed for prosthesis operation. Experiments with five subjects show that the system can achieve an overall recognition accuracy of 95.8%

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