Electrical impedance tomography for pulmonary function monitoring without dorsal electrodes.

Electrical impedance tomography (EIT) is a non-invasive technology that can visualize conductivity changes inside human body in real time using multiple surface electrodes. For convenience of wearing and application, if no electrodes are attached to the back, a commonly used image reconstruction approach produces poor images of dorsal region. In this study, we developed a special current injection and voltage measurement pattern to well reconstruct the conductivity distribution inside the body even in the absence of dorsal electrodes. The proposed method has proven through numerical and phantom experiments.

On the measurement of skeletal muscle anisotropic permittivity property with a single cross-shaped needle insertion.

Application of minimally invasive methods to enable the measurement of tissue permittivity in the neuromuscular clinic remain elusive. This paper provides a theoretical and modeling study on the measurement of the permittivity of two-dimensional anisotropic tissues such as skeletal muscle with a multi-electrode cross-shaped needle. For this, we design a novel cross-shaped needle with multiple-electrodes and analyse apparent impedance corresponding to the measured impedance. In addition, we propose three methods of estimate anisotropic muscle permittivity. Compared to existing electrical impedance-based needle methods that we have developed, the new needle design and numerical methods associated enable estimating in vivo muscle permittivity values with only a single needle insertion. Being able to measure muscle permittivity directly with a single needle insertion could open up an entirely new area of research with direct clinical application, including using these values to assist in neuromuscular diagnosis and to assess subtle effects of therapeutic intervention on muscle health.

Modeling and Reproducibility of Twin Concentric Electrical Impedance Myography.

OBJECTIVE: Electrical impedance myography (EIM) is a recent technology to assess muscle health. As of today, the clinical application of EIM has been applied only to evaluate muscle condition using non-invasive surface electrodes in contact with the skin; however, intermediate tissues at the recording site introduce confounding artifacts which reduce the technique's performance as a biomarker of neuromuscular disorders (NMD). Here, we develop and test in humans a new approach using two concentric needles for intramuscular EIM recordings. METHODS: First, we study the recording characteristics of dual concentric needle EIM via analytical models and finite element models (FEMs). Next, the validity of the models is verified by performing experiments on saline and agar phantoms. Finally, 8 subjects with various neuromuscular diseases were studied measuring tibialis anterior, biceps, deltoid, adductor pollicis brevis, first dorsal interosseous and flexor carpi radialis muscles. RESULTS: Analytical and FEM simulations are in good agreement with a maximum experimental discrepancy 8% and 9% using gauge needles 26 and 30, respectively. The inter-session reproducibility, as measured by the intraclass correlation coefficients for all muscles studied, was 0.926, which is comparable or exceeds the reproducibility of other well-established electrophysiological tests to assess muscle health. CONCLUSION: The reproducibility of the technique support future clinical validation of needle EIM for assessment of disease status, either as part of standard patient care or as biomarker measure in clinical trials. SIGNIFICANCE: Needle EIM has the potential of becoming a valuable diagnostic tool to evaluate NMD in adult population.

Low frequency conductivity reconstruction based on a single current injection via MREIT.

Conventional magnetic resonance electrical impedance tomography (MREIT) reconstruction methods require administration of two linearly independent currents via at least two electrode pairs. This requires long scanning times and inhibits coordination of MREIT measurements with electrical neuromodulation strategies. We sought to develop an isotropic conductivity reconstruction algorithm in MREIT based on a single current injection, both to decrease scanning time by a factor of two and enable MREIT measurements to be conveniently adapted to general transcranial- or implanted-electrode neurostimulation protocols. In this work, we propose and demonstrate an iterative algorithm that extends previously published MREIT work using two-current administration approaches. The proposed algorithm is a single-current adaptation of the harmonic B z algorithm. Forward modeling of electric potentials is used to capture changes of conductivity along current directions that would normally be invisible using data from a single-current administration. Computational and experimental results show that the reconstruction algorithm is capable of reconstructing isotropic conductivity images that agree well in terms of L 2 error and structural similarity with exact conductivity distributions or two-current-based MREIT reconstructions. We conclude that it is possible to reconstruct high quality electrical conductivity images using MREIT techniques and one current injection only.

Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning.

This paper proposes a deep learning method based on electrical impedance tomography (EIT) to estimate the thickness of abdominal subcutaneous fat. EIT for evaluating the thickness of abdominal subcutaneous fat is an absolute imaging problem that aims at reconstructing conductivity distributions from current-to-voltage data. Existing reconstruction methods based on EIT have difficulty handling the inherent drawbacks of strong nonlinearity and severe ill-posedness of EIT; hence, absolute imaging may not be possible using linearized methods. To handle nonlinearity and ill-posedness, we propose a deep learning method that finds useful solutions within a restricted admissible set by accounting for prior information regarding abdominal anatomy. We determined that a specially designed training dataset used during the deep learning process significantly reduces ill-posedness in the absolute EIT problem. In the preprocessing stage, we normalize current-voltage data to alleviate the effects of electrodeposition and body geometry by exploiting knowledge regarding electrode positions and body geometry. The performance of the proposed method is demonstrated through numerical simulations and phantom experiments using a 10 channel EIT system and a human-like domain.

Separation of Subcutaneous Fat From Muscle in Surface Electrical Impedance Myography Measurements Using Model Component Analysis.

OBJECTIVE: Electrical impedance myography (EIM) is a relatively new technique to assess neuromuscular disorders (NMD). Although the application of EIM using surface electrodes (sEIM) has been adopted by the neurology community in recent years to evaluate NMD status, sEIM's sensitivity as a biomarker of skeletal muscle condition is impacted by subcutaneous fat (SF) tissue. Here, we develop a method that is able to remove the contribution of SF from sEIM data. METHODS: We evaluate independent component analysis (ICA) and principal component analysis (PCA) for this purpose. Then, we introduce the so-called model component analysis (MCA). All methods are validated with numerical simulations using impedivity data from SF and muscle tissues. The methods are then tested with measurements performed in diseased individuals ( n=3). RESULTS: Simulations demonstrate that MCA is the most accurate method at separating the impedivity of SF and muscle tissues with the accuracy being 99.2%, followed by ICA with 51.4%, and finally PCA with 38.5%. Experimental results from sEIM data measured on the triceps brachii of patients are consistent with muscle grayscale level values obtained using ultrasound imaging. CONCLUSION: MCA can be used to separate the impedivity of SF and muscle tissues from sEIM data, thus increasing the sensitivity to detect changes in the muscle. SIGNIFICANCE: MCA can make the sEIM technique a better diagnostic tool and biomarker of disease progression and response to therapy by removing the confounding effect of SF tissue in NMD patients with excess subcutaneous fat tissue for any reason.

Electrical impedance imaging of human muscle at the microscopic scale using a multi-electrode needle device: A simulation study.

OBJECTIVE: To use a standard modeling approach to evaluate the feasibility of imaging healthy and diseased skeletal muscle at the microscopic scale with a novel electrical impedance imaging (EII) needle. METHODS: We modeled an EII needle containing 16 impedance electrodes arranged circumferentially around the shaft of a non-conductive 19-gauge needle in 4 planes. We then combined the finite element method approach with a reconstruction algorithm to create imaging simulations of the electrical properties of the triceps brachii by localized intramuscular fat (as might be seen in any chronic neuromuscular disease) and by localized edema (as in inflammatory myositis or after direct muscle injury). RESULTS: We were able to image a 1 cm radial region of interest with a resolution of 200 µm. Modeling localized deposition of fat and pockets of inflammatory cells, showing clear differences between the two modeled clinical states. CONCLUSIONS: This modeling study shows needle EII's ability to image the internal composition of muscle. These results can serve as an initial guide in designing and manufacturing prototype EII needles for experimental testing in animals and eventually in humans. SIGNIFICANCE: Needle EII could serve as a new minimally invasive technique for imaging human muscle at the microscopic scale, potentially serving as a new biomarker to assess disease response to therapy.

A local region of interest imaging method for electrical impedance tomography with internal electrodes.

Electrical Impedance Tomography (EIT) is a very attractive functional imaging method despite the low sensitivity and resolution. The use of internal electrodes with the conventional reconstruction algorithms was not enough to enhance image resolution and accuracy in the region of interest (ROI). We propose a local ROI imaging method with internal electrodes developed from careful analysis of the sensitivity matrix that is designed to reduce the sensitivity of the voxels outside the local region and optimize the sensitivity of the voxel inside the local region. We perform numerical simulations and physical measurements to demonstrate the localized EIT imaging method. In preliminary results with multiple objects we show the benefits of using an internal electrode and the improved resolution due to the local ROI image reconstruction method. The sensitivity is further increased by allowing the surface electrodes to be unevenly spaced with a higher density of surface electrodes near the ROI. Also, we analyse how much the image quality is improved using several performance parameters for comparison. While these have not yet been studied in depth, it convincingly shows an improvement in local sensitivity in images obtained with an internal electrode in comparison to a standard reconstruction method.

Effective admittivity of biological tissues as a coefficient of elliptic PDE.

The electrical properties of biological tissues can be described by a complex tensor comprising a simple expression of the effective admittivity. The effective admittivities of biological tissues depend on scale, applied frequency, proportions of extra- and intracellular fluids, and membrane structures. The effective admittivity spectra of biological tissue can be used as a means of characterizing tissue structural information relating to the biological cell suspensions, and therefore measuring the frequency-dependent effective conductivity is important for understanding tissue's physiological conditions and structure. Although the concept of effective admittivity has been used widely, it seems that its precise definition has been overlooked. We consider how we can determine the effective admittivity for a cube-shaped object with several different biologically relevant compositions. These precise definitions of effective admittivity may suggest the ways of measuring it from boundary current and voltage data. As in the homogenization theory, the effective admittivity can be computed from pointwise admittivity by solving Maxwell equations. We compute the effective admittivity of simple models as a function of frequency to obtain Maxwell-Wagner interface effects and Debye relaxation starting from mathematical formulations. Finally, layer potentials are used to obtain the Maxwell-Wagner-Fricke expression for a dilute suspension of ellipses and membrane-covered spheres.