Correction of electrode modelling errors in multi-frequency EIT imaging

Exploratory study of a multifrequency EIT-based method for detecting intracranial abnormalities

Objective

The purpose of this paper is to compare the differences in the features of multifrequency electrical impedance tomography (MFEIT) images of human heads between healthy subjects and patients with brain diseases and to explore the possibility of applying MFEIT to intracranial abnormality detection.

Methods

Sixteen healthy volunteers and 8 patients with brain diseases were recruited as subjects, and the cerebral MFEIT data of 9 frequencies in the range of 21 kHz - 100 kHz of all subjects were acquired with an MFEIT system. MFEIT image sequences were obtained according to certain imaging algorithms, and the area ratio of the ROI (AR_ROI) and the mean value of the reconstructed resistivity change of the ROI (MVRRC_ROI) on both the left and right sides of these images were extracted. The geometric asymmetry index (GAI) and intensity asymmetry index (IAI) were further proposed to characterize the symmetry of MFEIT images based on the extracted indices and to statistically compare and analyze the differences between the two groups of subjects on MFEIT images.

Results

There were no significant differences in either the AR_ROI or the MVRRC_ROI between the two sides of the brains of healthy volunteers (p > 0.05); some of the MFEIT images mainly in the range of 30 kHz - 60 kHz of patients with brain diseases showed stronger resistivity distributions (larger area or stronger signal) that were approximately symmetric with the location of the lesions. However, statistical analysis showed that the AR_ROI and the MVRRC_ROI on the healthy sides of MFEIT images of patients with unilateral brain disease were not significantly different from those on the affected side (p > 0.05). The GAI and IAI were higher in all patients with brain diseases than in healthy volunteers except for 80 kHz (p < 0.05).

Conclusion

There were significant differences in the geometric symmetry and the signal intensity symmetry of the reconstructed targets in the MFEIT images between healthy volunteers and patients with brain diseases, and the above findings provide a reference for the rapid detection of intracranial abnormalities using MFEIT images and may provide a basis for further exploration of MFEIT for the detection of brain diseases.

Advances in electrical impedance tomography-based brain imaging

Novel advances in the field of brain imaging have enabled the unprecedented clinical application of various imaging modalities to facilitate disease diagnosis and treatment. Electrical impedance tomography (EIT) is a functional imaging technique that measures the transfer impedances between electrodes on the body surface to estimate the spatial distribution of electrical properties of tissues. EIT offers many advantages over other neuroimaging technologies, which has led to its potential clinical use. This qualitative review provides an overview of the basic principles, algorithms, and system composition of EIT. Recent advances in the field of EIT are discussed in the context of epilepsy, stroke, brain injuries and edema, and other brain diseases. Further, we summarize factors limiting the development of brain EIT and highlight prospects for the field. In epilepsy imaging, there have been advances in EIT imaging depth, from cortical to subcortical regions. In stroke research, a bedside EIT stroke monitoring system has been developed for clinical practice, and data support the role of EIT in multi-modal imaging for diagnosing stroke. Additionally, EIT has been applied to monitor the changes in brain water content associated with cerebral edema, enabling the early identification of brain edema and the evaluation of mannitol dehydration. However, anatomically realistic geometry, inhomogeneity, cranium completeness, anisotropy and skull type, etc., must be considered to improve the accuracy of EIT modeling. Thus, the further establishment of EIT as a mature and routine diagnostic technique will necessitate the accumulation of more supporting evidence.

Self-Abrading Servo Electrode Helmet for Electrical Impedance Tomography

Electrical Impedance Tomography (EIT) is a medical imaging technique which has the potential to reduce time to treatment in acute stroke by rapidly differentiating between ischaemic and haemorrhagic stroke. The potential of these methods has been demonstrated in simulation and phantoms, it has not yet successfully translated to clinical studies, due to high sensitivity to errors in scalp electrode mislocation and poor electrode-skin contact. To overcome these limitations, a novel electrode helmet was designed, bearing 32 independently controlled self-abrading electrodes. The contact impedance was reduced through rotation on an abrasive electrode on the scalp using a combined impedance, rotation and position feedback loop. Potentiometers within each unit measure the electrode tip displacement within 0.1 mm from the rigid helmet body. Characterisation experiments on a large-scale test rig demonstrated that approximately 20 kPa applied pressure and 5 rotations was necessary to achieve the target 5 kΩ contact impedance at 20 Hz. This performance was then replicated in a simplified self-contained unit where spring loaded electrodes are rotated by servo motors. Finally, a 32-channel helmet and controller which sequentially minimised contact impedance and simultaneously located each electrode was built which reduced the electrode application and localisation time to less than five minutes. The results demonstrated the potential of this approach to rapidly apply electrodes in an acute setting, removing a significant barrier for imaging acute stroke with EIT.

Reconstruction algorithm for frequency-differential EIT using absolute values

OBJECTIVE

Tissues in the body differ by their frequency-dependent conductivity. Frequency-differential electrical impedance tomography (fdEIT) is a promising technique to reconstruct the distribution of tissue inside the body by injecting current at two frequencies and measuring the resulting surface-potential.

APPROACH

The Gauss-Newton method is one way to map the surface measurements to a conductivity image. Usually, the minimization function contains only weighted differential measurement data and a regularization. This traditional method is extended by absolute measurement data to improve fdEIT reconstruction results. The key challenge of unknown torso geometries and electrode displacement has been addressed for the reconstruction of different lung pathologies.

MAIN RESULTS

The frequency-dependent conductivity of the background was reconstructed precisely and a contrast between organs was achieved. The algorithm shows good performance compared to GREIT and the traditional Gauss-Newton method with respect to the figures of merit of GREIT.

SIGNIFICANCE

The reconstruction is robust in the presence of noise. One application of the algorithm might be the detection and monitoring of lung diseases like edema or atelectasis.

Addition of internal electrodes is beneficial for focused bioimpedance measurements in the lung

OBJECTIVE

Bioimpedance measurements such as bioimpedance spectroscopy (BIS) or electrical impedance tomography (EIT) are used in many biomedical applications. While BIS measures and analyzes the impedance in a frequency range at constant electrode positions, EIT aims to reconstruct images of the conductivity distribution from multiple measurements at different electrode positions. Our aim is to add spatial information to tetrapolar BIS measurements by using electrode positions that focus measurements on desired regions of interest. In this paper, we aim to investigate, whether internal electrodes that can be integrated into breathing or gastroesophageal tubes, can improve the local sensitivity of bioimpedance spectroscopy measurements.

APPROACH

We present the results of a simulation study, in which we investigated more than 4 M different electrode configurations on their ability to monitor specific regions of interest (ROI) in the lung. Based on the sensitivity, which describes the impact of a conductivity change on the measured impedance, we define three main criteria which we use to evaluate our simulation results: the selectivity [Formula: see text], which describes the impact of a conductivity change inside the region of interest compared to a conductivity change outside the ROI; the homogeneity [Formula: see text], which describes the distribution of the sensitivity inside the ROI; and the absolute impedance contribution ratio [Formula: see text], which describes the contribution of the ROI to the measured impedance.

MAIN RESULTS

Depending on the region of interest, electrode configurations using internal electrodes are between 9.8 % and 90 % better with respect to these criteria than configurations using external electrodes only.

SIGNIFICANCE

The combination of internal and external electrodes improves the focusing ability of tetrapolar impedance measurements on specific lung regions, which may be especially beneficial for lung monitoring in intensive care.

Ex-Vivo Characterization of Bioimpedance Spectroscopy of Normal, Ischemic and Hemorrhagic Rabbit Brain Tissue at Frequencies from 10 Hz to 1 MHz

Stroke is a severe cerebrovascular disease and is the second greatest cause of death worldwide. Because diagnostic tools (CT and MRI) to detect acute stroke cannot be used until the patient reaches the hospital setting, a portable diagnostic tool is urgently needed. Because biological tissues have different impedance spectra under normal physiological conditions and different pathological states, multi-frequency electrical impedance tomography (MFEIT) can potentially detect stroke. Accurate impedance spectra of normal brain tissue (gray and white matter) and stroke lesions (ischemic and hemorrhagic tissue) are important elements when studying stroke detection with MFEIT. To our knowledge, no study has comprehensively measured the impedance spectra of normal brain tissue and stroke lesions for the whole frequency range of 1 MHz within as short as possible an ex vivo time and using the same animal model. In this study, we established intracerebral hemorrhage and ischemic models in rabbits, then measured and analyzed the impedance spectra of normal brain tissue and stroke lesions ex vivo within 15 min after animal death at 10 Hz to 1 MHz. The results showed that the impedance spectra of stroke lesions significantly differed from those of normal brain tissue; the ratio of change in impedance of ischemic and hemorrhagic tissue with regard to frequency was distinct; and tissue type could be discriminated according to its impedance spectra. These findings further confirm the feasibility of detecting stroke with MFEIT and provide data supporting further study of MFEIT to detect stroke.

Improved electrode positions for local impedance measurements in the lung-a simulation study

Impedance spectroscopy can be used to analyze the dielectric properties of various materials. In the biomedical domain, it is used as bioimpedance spectroscopy (BIS) to analyze the composition of body tissue. Being a non-invasive, real-time capable technique, it is a promising modality, especially in the field of lung monitoring. Unfortunately, up to now, BIS does not provide any regional lung information as the electrodes are usually placed in hand-to-hand or transthoracic configurations. Even though transthoracic electrode configurations are in general capable of monitoring the lung, no focusing to specific regions is achieved. In order to resolve this issue, we use a finite element model (FEM) of the human body to study the effect of different electrode configurations on measured BIS data. We present evaluation results and show suitable electrode configurations for eight lung regions. We show that, using these optimized configurations, BIS measurements can be focused to desired regions allowing local lung analysis.