Symmetry difference electrical impedance tomography—a novel modality for anomaly detection

McDermott, Barry; Porter, Emily; Jones, Marggie; McGinley, Brian; O’Halloran, Martin

doi:10.1088/1361-6579/aab656

Cited by 6 publications

(8 citation statements)

References 27 publications

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“…EIT-based applications in neural and intracranial imaging continue to be explored. McDermott et al's (2018) work in symmetric difference EIT is explicitly focused on detecting intracranial lesions for use in stroke assessment, while Faulkner et al's (2018) work characterizes the frequency response of rat brain impedance changes during evoked physiological activity. Finally, Hope et al's (2018) description of a model for using EIT for neuronal-prosthetic control represents a novel EIT-application that leverages the emerging field of EIT-based neuronal imaging.…”

Section: Exploration Of Clinical Applicationsmentioning

confidence: 99%

Focus on advances in electrical impedance tomography

Bayford

Halter

2018

Physiol. Meas.

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Section: Exploration Of Clinical Applicationsmentioning

confidence: 99%

Focus on advances in electrical impedance tomography

Bayford

Halter

2018

Physiol. Meas.

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“…Recently we have introduced a novel modality of EIT suitable for static scenes featuring symmetry, which focusses on detection of unilateral perturbations causing a change in this inherent symmetry (McDermott et al 2018b(McDermott et al , 2019b. This so-called bi-frequency symmetry difference EIT (BFSD-EIT) was then tested for the application of stroke (McDermott et al 2019c).…”

Section: Introductionmentioning

confidence: 99%

Multi-frequency symmetry difference electrical impedance tomography with machine learning for human stroke diagnosis

et al. 2020

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Multi-Frequency Symmetry Difference Electrical Impedance Tomography (MFSD-EIT) can robustly detect and identify unilateral perturbations in symmetric scenes. Here, an investigation is performed to assess if the algorithm can be successfully applied to identify the aetiology of stroke with the aid of machine learning. Methods: Anatomically realistic fourlayer Finite Element Method models of the head based on stroke patient images are developed and used to generate EIT data over a 5 Hz -100 Hz frequency range with and without bleed and clot lesions present. Reconstruction generates conductivity maps of each head at each frequency. Application of a quantitative metric assessing changes in symmetry across the sagittal plane of the reconstructed image and over the frequency range allows lesion detection and identification. The algorithm is applied to both simulated and human (n=34 subjects) data. A classification algorithm is applied to the metric value in order to differentiate between normal, haemorrhage and clot values. Results: An average accuracy of 85% is achieved when MFSD-EIT with Support Vector Machines (SVM) classification is used to identify and differentiate bleed from clot in human data, with 77% accuracy when differentiating normal from stroke in human data. Conclusion: Applying a classification algorithm to metrics derived from MFSD-EIT images is a novel and promising technique for detection and identification of perturbations in static scenes. Significance: The MFSD-EIT algorithm used with machine learning gives promising results of lesion detection and identification in challenging conditions like stroke. The results imply feasible translation to human patients.

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“…Middle cerebral artery occlusion and bilateral common carotid artery occlusion (BCCAO) methods have been previously reported to mimic focal and global ischemic conditions, respectively, in the rodent model (Iwasaki et al 1989, Parmentier et al 1999, Wang et al 2015. Nowadays, electrical impedance spectroscopy is an emerging tool to differentiate between normal and stroke conditions (Dowrick et al 2016, Yang et al 2016, McDermott et al 2018. Bio-impedance analysis is a non-invasive method used to measure the linearly varying electrical response across a specific dielectric medium (such as cells, tissue, etc), to yield useful information about that particular medium (Anon 1996).…”

Section: Introductionmentioning

confidence: 99%

Changes in electrolyte concentrations alter the impedance during ischemia-reperfusion injury in rat brain

et al. 2019

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Objective: Ischemic stroke is a major cause of death and disability worldwide. Nowadays, electrical impedance spectroscopy is an emerging tool to differentiate between normal and stroke conditions. Approach: In this study, changes in the bio-impedance spectroscopy using a two-electrode method with varying frequencies from 100 to 35 kHz have been assessed in a model of global cerebral ischemia in anesthetized rats during normal, occlusion and reperfusion conditions. Global cerebral ischemia was induced by bilateral common carotid artery occlusion for 40 min following 40 min of reperfusion. The concentration of sodium, potassium, calcium and chloride ions in the whole rat brain was determined by electrolyte analyzer. For the interpretation of in vivo results, changes in electrical impedance with varying concentrations of sodium, potassium and calcium ions in artificial cerebrospinal fluid (aCSF) were also observed using the bio-impedance spectroscopy method. Main results: The in vivo bio-impedance analysis suggests that the impedance is consistently increased during occlusion as compared to the normal condition. The in vitro study revealed that the impedance escalates with an increase in the concentration of potassium and calcium ions and reduces with an increase in the concentration of sodium ions in aCSF. A further electrolyte analysis suggested that the level of sodium and chloride ions is significantly decreased and the level of calcium and potassium is significantly increased during occlusion as compared to the normal condition. Significance: These findings suggest that the increase in impedance during occlusion may be due to changes in the ionic concentration of the rat brain. The above in vivo and in vitro studies successfully demonstrated and interrelated the change in impedance with corresponding changes in ionic concentration.

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Symmetry difference electrical impedance tomography—a novel modality for anomaly detection

Cited by 6 publications

References 27 publications

Focus on advances in electrical impedance tomography

Focus on advances in electrical impedance tomography

Multi-frequency symmetry difference electrical impedance tomography with machine learning for human stroke diagnosis

Changes in electrolyte concentrations alter the impedance during ischemia-reperfusion injury in rat brain

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