Rebecca J. Yerworth

Multi-frequency electrical impedance tomography (EIT) of the adult human head: initial findings in brain tumours, arteriovenous malformations and chronic stroke, development of an analysis method and calibration

MFEIT (multi-frequency electrical impedance tomography) could distinguish between ischaemic and haemorrhagic stroke and permit the urgent use of thrombolytic drugs in patients with ischaemic stroke. The purpose of this study was to characterize the UCLH Mk 2 MFEIT system, designed for this purpose, with 32 electrodes and a multiplexed 2 kHz to 1.6 MHz single impedance measuring circuit. Data were collected in seven subjects with brain tumours, arteriovenous malformations or chronic stroke, as these resembled the changes in haemorrhagic or ischaemic stroke. Calibration studies indicated that the reliable bandwidth was only 16-64 kHz because of front-end components placed to permit simultaneous EEG recording. In raw in-phase component data, the SD of 16-64 kHz data for one electrode combination across subjects was 2.45 +/- 0.9%, compared to a largest predicted change of 0.35% estimated using the FEM of the head. Using newly developed methods of examining the most sensitive channels from the FEM, and nonlinear imaging constrained to the known site of the lesion, no reproducible changes between pathologies were observed. This study has identified a specification for accuracy in EITS in acute stroke, identified the size of variability in relation to this in human recordings, and presents new methods for analysis of data. Although no reproducible changes were identified, we hope this will provide a foundation for future studies in this demanding but potentially powerful novel application.

Generating accurate finite element meshes for the forward model of the human head in EIT

The use of realistic anatomy in the model used for image reconstruction in EIT of brain function appears to confer significant improvements compared to geometric shapes such as a sphere. Accurate model geometry may be achieved by numerical models based on magnetic resonance images (MRIs) of the head, and this group has elected to use finite element meshing (FEM) as it enables detailed internal anatomy to be modelled and has the capability to incorporate information about tissue anisotropy. In this paper a method for generating accurate FEMs of the human head is presented where MRI images are manually segmented using custom adaptation of industry standard commercial design software packages. This is illustrated with example surface models and meshes from adult epilepsy patients, a neonatal baby and a phantom latex tank incorporating a real skull. Mesh quality is assessed in terms of element stretch and hence distortion.

Electrical impedance tomography spectroscopy (EITS) for human head imaging.

Electrical impedance tomography (EIT) is a recently developed medical imaging method which has practical advantages for imaging brain function as it is inexpensive, rapid and portable. Its principal use in validated human studies to date has been to image changes in impedance at a single excitation frequency over time, but there are potential applications where it is desirable to obtain images from a single point in time, which could be achieved by imaging over multiple frequencies. We describe a novel multifrequency EIT design which provides up to 64 electrodes for imaging in the head. This was achieved by adding a multiplexer to a single channel of an existing system, the Sheffield Mark 3.5. This provides a flexible protocol for addressing up to 64 electrodes but CMRR decreases from 90 dB to 80 dB and analogue amplifier bandwidth from > 1.6 MHz to 0.8 MHz. This did not significantly affect performance, as cylinders of banana, 10% of the diameter of a saline filled spherical tank, could be visualized with frequency referenced imaging. The design appears to have been an acceptable compromise between practicality and performance and will now be employed in clinical trials of multifrequency EIT in stroke, epilepsy and neonatal brain injury.

Design and performance of the UCLH Mark 1b 64 channel electrical impedance tomography (EIT) system, optimized for imaging brain function

The UCLH Mark 1b is a portable EIT system that can address up to 64 electrodes, which has been designed for imaging brain function with scalp electrodes. It employs a single impedance-measuring circuit and multiplexer so that electrode combinations may be addressed flexibly using software. It operates in the relatively low frequency band between 225 Hz and 77 kHz, as lower frequencies produce larger changes during brain activity, and has a videocassette-sized headbox on a lead 10 m long, connected to a base box the size of a video recorder, and notebook PC, so that recordings may be made in ambulant subjects. Its performance was assessed using a resistor-capacitor network, and two saline-filled tanks-a cylindrical Perspex one and a latex one which contained a human skull. System signal-to-noise ratio was better than 50 dB and the maximum reciprocity error less than 10% for most frequencies. The CMMR was better than 80 dB at 38 kHz and a sponge, 20 mm across, which caused a local 12% impedance increase, was correctly localized in images. This suggests that the system has adequate performance to image impedance changes of 5-50% known to occur in the brain during normal activity, epilepsy or stroke; clinical trials to image these conditions are in progress.

A comparison of headnet electrode arrays for electrical impedance tomography of the human head.

Three types of commercially available headnet electrode arrays, designed for use in EEG, and conventional EEG Ag/AgCl cup electrodes were tested on human subjects, and a realistic, saline-filled head-shaped tank was prepared with vegetable skin to simulate human skin in order to determine the optimum electrode system for electrical impedance tomography (EIT) of the human head. Impedance changes during EIT acquisition were produced in healthy volunteers during a finger-thumb apposition task and in tanks by the insertion of a Perspex rod. Signal-to-baseline noise, measured from raw EIT data, was 2.3 +/- 0.3 and 2.3 +/- 0.2 for the human and tank data, respectively. In both the human and tank experiments, a commercial hydrogel elasticated electrode headnet produced the least amount of baseline noise, and was the only headnet in the human data with noise levels acceptable for EIT imaging. Image quality measured in the tank was similar for most of the headnets tested, except that the EEG electrodes produced a higher positional error and electrodes in a geodesic elasticated net produced images with worse subjective image quality. Overall, the hydrogel elasticated headnet was judged to be the most suitable for human neuroimaging with EIT.

Development of a neonate lung reconstruction algorithm using a wavelet AMG and estimated boundary form

Objective, non-invasive measures of lung maturity and development, oxygen requirements and lung function, suitable for use in small, unsedated infants, are urgently required to define the nature and severity of persisting lung disease, and to identify risk factors for developing chronic lung problems. Disorders of lung growth, maturation and control of breathing are among the most important problems faced by the neonatologists. At present, no system for continuous monitoring of neonate lung function to reduce the risk of chronic lung disease in infancy in intensive care units exists. We are in the process of developing a new integrated electrical impedance tomography (EIT) system based on wearable technology to integrate measures of the boundary diameter from the boundary form for neonates into the reconstruction algorithm. In principle, this approach could provide a reduction of image artefacts in the reconstructed image associated with incorrect boundary form assumptions. In this paper, we investigate the required accuracy of the boundary form that would be suitable to minimize artefacts in the reconstruction for neonate lung function. The number of data points needed to create the required boundary form is automatically determined using genetic algorithms. The approach presented in this paper is to assist quality of the reconstruction using different approximations to the ideal boundary form. We also investigate the use of a wavelet algebraic multi-grid (WAMG) preconditioner to reduce the reconstruction computation requirements. Results are presented that demonstrate a full 3D model is required to minimize artefact in the reconstructed image and the implementation of a WAMG for EIT.

The application of the generalized vector sample pattern matching method for EIT image reconstruction

This paper presents a new application of a generalized vector sample pattern matching (GVSPM) method for image reconstruction of conductivity changes in electrical impedance tomography. GVSPM is an iterative method for linear inverse problems. The key concept of the GVSPM is that the objective function is defined in terms of an angular component between the inner product of the known vector and solution of a system of equations. Comparisons are presented between images of simulated and experimental data, reconstructed using truncated singular value decomposition and GVSPM. In both cases, a normalized sensitivity matrix is constructed using the finite volume method to solve the forward problem.

A method for removing artefacts from continuous EEG recordings during functional electrical impedance tomography for the detection of epileptic seizures.

Electrical impedance tomography (EIT) is a portable, non-invasive medical imaging method, which could be employed to image the seizure onset in subjects undergoing assessment prior to epilepsy surgery. Each image is obtained from impedance measurements conducted with imperceptible current at tens of kHz. For concurrent imaging with video electroencephalogram (EEG), the EIT introduces a substantial artefact into the EEG due to current switching at frequencies in the EEG band. We present here a method for its removal, so that EIT and the EEG could be acquired simultaneously. A low-pass analogue filter for EEG channels (-6 dB at 48 Hz) and a high-pass filter (-3 dB at 72 Hz) for EIT channels reduced the artefact from 2-3 mV to 50-300 microV, but still left a periodic artefact at about 3 Hz. This was reduced to less than 10 microV with a software filter, which subtracted an artefact template from the EEG raw traces. The EEG was made clinically acceptable at four times its acquisition speed. This method could enable EIT to become a technique for imaging on telemetry units alongside EEG, without interfering with routine EEG reporting.

Use of statistical parametric mapping (SPM) to enhance electrical impedance tomography (EIT) image sets

Use of statistical parametric mapping (SPM), which is widely used in analysis of neuroimaging studies with fMRI and PET, has the potential to improve quality of EIT images for clinical use. Minimal modification to SPM is needed, but statistical analysis based on height, not extent thresholds, should be employed, due to the 20-80% variation of the point spread function, across EIT images. SPM was assessed in EIT images reconstructed with a linear time difference algorithm utilizing an anatomically realistic finite element model of the human head. Images of the average of data sets were compared with those produced using SPM over 10-40 individual image data sets without averaging. For a point disturbance, a sponge 15% of the diameter of an anatomically realistic saline-filled tank including a skull, with a contrast of 15%, and for visual evoked response data in 14 normal human volunteers, images produced with SPM were less noisy than the average images. For the human data, no consistent physiologically realistic changes were seen with either SPM or direct reconstruction; however, only a small data set was available, limiting the power of the SPM analysis. SPM may be used on EIT images and has the potential to extract improved images from clinical data series with a low signal-to-noise ratio.

Reconstruction algorithms to monitor neonate lung function

Disorders of lung growth, maturation and control of breathing are among the most important problems faced by the neonatologist. Objective, non-invasive measures of lung maturity and development, oxygen requirements and lung function, suitable for use in small, unsedated infants, are urgently required to define the nature and severity of persisting lung disease, and to identify risk factors for developing chronic lung problems. At present, no system for continuous monitoring of neonate lung function to reduce the risk of CLDI in intensive care units (ITUs) exists.We present the development of image reconstruction algorithms to monitor neonate lung function in ITU’s, and a method base on wearable technology to integrate measures of the boundary diameter from the boundary form. This approach provides a reduction of image artefacts in the reconstructed image associated with incorrect boundary form assumptions. In terms of image reconstruction, we utilise the concept of subspace invariance to design a block adaptive preconditioning scheme, which yields a smaller error norm and can provide improvements in the condition number of the coefficients matrix, as compared to incomplete Cholesky factorization, followed by the application of conjugate gradient.