Absolute Reconstructions Using Rotational Electrical Impedance Tomography for Breast Cancer Imaging

Assessing pulmonary function in ALS using electrical impedance tomography

Objective: We sought to determine whether thoracic electrical impedance tomography (EIT) could characterize pulmonary function in amyotrophic lateral sclerosis (ALS) patients, including those with facial weakness. Thoracic EIT is a noninvasive, technology in which a multi-electrode belt is placed across the chest, producing real-time impedance imaging of the chest during breathing. Methods: We enrolled 32 ALS patients and 32 age- and sex-matched healthy controls (HCs) without underlying lung disease. All participants had EIT measurements performed simultaneously with standard pulmonary function tests (PFTs), including slow and forced vital capacity (SVC and FVC) in upright and supine positions and maximal inspiratory and expiratory pressures (MIPs and MEPs, respectively). Intraclass correlation coefficients (ICCs) were calculated to assess the immediate reproducibility of EIT measurements and Pearson's correlations were used to explore the relationships between EIT and PFT values. Results: Data from 30 ALS patients and 27 HCs were analyzed. Immediate upright SVC reproducibility was very high (ICC 0.98). Correlations were generally strongest between EIT and spirometry measures, with R values ranging from 0.64 to 0.82 (p < 0.001) in the ALS cohort. There were less robust correlations between EIT values and both MIPs and MEPs in the ALS patients, with R values ranging from 0.33 to 0.44. There was no significant difference for patients with and without facial weakness. There were no reported adverse events. Conclusion: EIT-based pulmonary measures hold the promise of providing an alternative approach for lung function assessment in ALS patients. Based on these early results, further development and study of this technology are warranted.

Cross modality generative learning framework for anatomical transitive Magnetic Resonance Imaging (MRI) from Electrical Impedance Tomography (EIT) image

This paper presents a cross-modality generative learning framework for transitive magnetic resonance imaging (MRI) from electrical impedance tomography (EIT). The proposed framework is aimed at converting low-resolution EIT images to high-resolution wrist MRI images using a cascaded cycle generative adversarial network (CycleGAN) model. This model comprises three main components: the collection of initial EIT from the medical device, the generation of a high-resolution transitive EIT image from the corresponding MRI image for domain adaptation, and the coalescence of two CycleGAN models for cross-modality generation. The initial EIT image was generated at three different frequencies (70 kHz, 140 kHz, and 200 kHz) using a 16-electrode belt. Wrist T1-weighted images were acquired on a 1.5T MRI. A total of 19 normal volunteers were imaged using both EIT and MRI, which resulted in 713 paired EIT and MRI images. The cascaded CycleGAN, end-to-end CycleGAN, and Pix2Pix models were trained and tested on the same cohort. The proposed method achieved the highest accuracy in bone detection, with 0.97 for the proposed cascaded CycleGAN, 0.68 for end-to-end CycleGAN, and 0.70 for the Pix2Pix model. Visual inspection showed that the proposed method reduced bone-related errors in the MRI-style anatomical reference compared with end-to-end CycleGAN and Pix2Pix. Multifrequency EIT inputs reduced the testing normalized root mean squared error of MRI-style anatomical reference from 67.9% ± 12.7% to 61.4% ± 8.8% compared with that of single-frequency EIT. The mean conductivity values of fat and bone from regularized EIT were 0.0435 ± 0.0379 S/m and 0.0183 ± 0.0154 S/m, respectively, when the anatomical prior was employed. These results demonstrate that the proposed framework is able to generate MRI-style anatomical references from EIT images with a good degree of accuracy.

Electrical Impedance Tomography: From the Traditional Design to the Novel Frontier of Wearables

Electrical impedance tomography (EIT) is a medical imaging technique based on the injection of a current or voltage pattern through electrodes on the skin of the patient, and on the reconstruction of the internal conductivity distribution from the voltages collected by the electrodes. Compared to other imaging techniques, EIT shows significant advantages: it does not use ionizing radiation, is non-invasive and is characterized by high temporal resolution. Moreover, its low cost and high portability make it suitable for real-time, bedside monitoring. However, EIT is also characterized by some technical limitations that cause poor spatial resolution. The possibility to design wearable devices based on EIT has recently given a boost to this technology. In this paper we reviewed EIT physical principles, hardware design and major clinical applications, from the classical to a wearable setup. A wireless and wearable EIT system seems a promising frontier of this technology, as it can both facilitate making clinical measurements and open novel scenarios to EIT systems, such as home monitoring.

Suppressing interferences of EIT on synchronous recording EEG based on comb filter for seizure detection

Background and objective

The purpose of this study was to eliminate the interferences of electrical impedance tomography (EIT) on synchronous recording electroencephalography (EEG) for seizure detection.

Methods

The simulated EIT signal generated by COMSOL Multiphysics was superimposed on the clinical EEG signal obtained from the CHB-MIT Scalp EEG Database, and then the spectrum features of superimposed mixed signals were analyzed. According to the spectrum analysis, in addition to high-frequency interference at 51.2 kHz related to the drive current, there was also low-frequency interference caused by switching of electrode pairs, which were used to inject drive current. A low pass filter and a comb filter were used to suppress the high-frequency interference and low-frequency interference, respectively. Simulation results suggested the low-pass filter and comb filter working together effectively filtered out the interference of EIT on EEG in the process of synchronous monitoring.

Results

As a result, the normal EEG and epileptic EEG could be recognized effectively. Pearson correlation analysis further confirmed the interference of EIT on EEG was effectively suppressed.

Conclusions

This study provides a simple and effective interference suppression method for the synchronous monitoring of EIT and EEG, which could be served as a reference for the synchronous monitoring of EEG and other medical electromagnetic devices.

Detection of subclinical hemorrhage using electrical impedance: a porcine study

OBJECTIVE

Analyze the performance of electrical impedance tomography (EIT) in an innovative porcine model of subclinical hemorrhage and investigate associations between EIT and hemodynamic trends.

APPROACH

Twenty-five swine were bled at slow rates to create an extended period of subclinical hemorrhage during which the animal's heart rate (HR) and blood pressure (BP) remained stable from before hemodynamic deterioration, where stable was defined as < 15% decrease in BP and < 20% increase in HR - i.e. hemorrhages were hidden from standard vital signs of HR and BP. Continuous vital signs, photo-plethysmography, and continuous non-invasive EIT data were recorded and analyzed with the objective of developing an improved means of detecting subclinical hemorrhage - ideally as early as possible.

MAIN RESULTS

Best area-under-the-curve (AUC) values from comparing bleed to no-bleed epochs were 0.96 at a 80 ml bleed (~15.4 minutes) using an EIT-data-based metric and 0.79 at a 120 ml bleed (~23.1 minutes) from invasively measured BP - i.e. the EIT-data-based metric achieved higher AUCs at earlier points compared to standard clinical metrics without requiring image reconstructions.

SIGNIFICANCE

In this clinically relevant porcine model of subclinical hemorrhage, EIT appears to be superior to standard clinical metrics in early detection of hemorrhage.

Electrical Impedance Tomography Technical Contributions for Detection and 3D Geometric Localization of Breast Tumors: A Systematic Review

Impedance measuring acquisition systems focused on breast tumor detection, as well as image processing techniques for 3D imaging, are reviewed in this paper in order to define potential opportunity areas for future research. The description of reported works using electrical impedance tomography (EIT)-based techniques and methodologies for 3D bioimpedance imaging of breast tissues with tumors is presented. The review is based on searching and analyzing related works reported in the most important research databases and is structured according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) parameters and statements. Nineteen papers reporting breast tumor detection and location using EIT were systematically selected and analyzed in this review. Clinical trials in the experimental stage did not produce results in most of analyzed proposals (about 80%), wherein statistical criteria comparison was not possible, such as specificity, sensitivity and predictive values. A 3D representation of bioimpedance is a potential tool for medical applications in malignant breast tumors detection being capable to estimate an ap-proximate the tumor volume and geometric location, in contrast with a tumor area computing capacity, but not the tumor extension depth, in a 2D representation.

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.

Diagnostic accuracy and prognostic value of three-dimensional electrical impedance tomography imaging in patients with breast cancer

Background

Three-dimensional electrical impedance tomography (3D EIT) is a novel, non-invasive, radiation-free imaging technology for breast cancer screening. This study aimed to identify characteristics and classification of 3D EIT breast cancer imaging that could provide diagnostic accuracy and prognostic value for breast cancer patients.

Methods

A total of 645 suspicious breast lesions [Breast Imaging Reporting and Data System (BI-RADS) III, IV, V] identified by mammography or ultrasound were examined with 3D EIT (MEIK, SIM-Technika, Yaroslavl, Russia). Breast tissue conductivity was quantified using MEIK 5.6 software. Diagnostic performance of visually interpreted 3D EIT was assessed using histology (surgical excision or vacuum core biopsy) and clinical follow-up. Kaplan-Meier analysis was used to calculate progression-free survival (PFS) and overall survival (OS) rates. Hazard ratio (HR) with a 95% confidence interval (95% CI) for various clinicopathological variables were determined using univariate and multivariate Cox regression models.

Results

Breast cancer was confirmed in 272 of 645 patients by histopathology and other diagnostic imaging modalities. Among the confirmed cases, 218 patients had positive 3D EIT findings. The sensitivity, specificity, accuracy, positive likelihood, and negative likelihood ratios of 3D EIT were 80.1%, 75.1%, 77.2%, 70.1%, and 83.8%. There were no significant differences in the diagnostic accuracy, sensitivity, or specificity between 3D EIT and mammography, ultrasound, or combined mammography and ultrasound. 3D EIT breast cancer images were classified into 3 different types, including Ia [non-complicated breast cancer (NCBC), 62 cases], Ib [complicated breast cancer (CBC), 131 cases], and Ic [edematous-infiltrative breast cancer (EIBC), 25 cases], which were associated with tumor size (P<0.001), TNM stage (P<0.001), and lymph node metastasis (P=0.012). At 5-year follow-up, multivariate analysis demonstrated that breast cancer 3D EIT imaging classification was an independent predictor for decreased OS (HR: 2.399, 95% CI: 1.035, 5.564, P=0.041) and PFS (HR: 2.836, 95% CI: 1.555, 5.172, P=0.012) in patients with breast cancer.

Conclusions

3D EIT breast cancer images were classified into 3 types based on different image characteristics. 3D EIT appeared to be useful in clinical diagnostic performance and prognostic evaluation in patients with breast cancer.

Robot-Assisted Electrical Impedance Scanning system for 2D Electrical Impedance Tomography tissue inspection

The electrical impedance tomography (EIT) technology is an important medical imaging approach to show the electrical characteristics and the homogeneity of a tissue region noninvasively. Recently, this technology has been introduced to the Robot Assisted Minimally Invasive Surgery (RAMIS) for assisting the detection of surgical margin with relevant clinical benefits. Nevertheless, most EIT technologies are based on a fixed multiple-electrodes probe which limits the sensing flexibility and capability significantly. In this study, we present a method for acquiring the EIT measurements during a RAMIS procedure using two already existing robotic forceps as electrodes. The robot controls the forceps tips to a series of predefined positions for injecting excitation current and measuring electric potentials. Given the relative positions of electrodes and the measured electric potentials, the spatial distribution of electrical conductivity in a section view can be reconstructed. Realistic experiments are designed and conducted to simulate two tasks: subsurface abnormal tissue detection and surgical margin localization. According to the reconstructed images, the system is demonstrated to display the location of the abnormal tissue and the contrast of the tissues' conductivity with an accuracy suitable for clinical applications.

Reconstruction of conductivity distribution with electrical impedance tomography based on hybrid regularization method

Purpose: Physiological or pathological variation would cause a change of conductivity. Electrical impedance tomography (EIT) is favorable in reconstructing conductivity distribution inside the detected area. However, the reconstruction is an ill-posed inverse problem and the spatial resolution of the reconstructed image is relatively poor. Approach: To deal with the problem, a regularization method is commonly applied. Traditional regularization methods have their own disadvantages. In this work, we develop an innovative hybrid regularization method to determine the conductivity distribution from the boundary measurement. To address the unwanted artifact observed in the total variation (TV) method, the proposed approach incorporates the TV method with the non-convex sparse penalty term-based wavelet transform. In the reconstruction, the sensitivity matrix is also normalized to increase the sensitivity of the measurement to the variation of the conductivity. The objective function is minimized with the split augmented Lagrangian shrinkage algorithm. Results: The feasibility of the proposed method is evaluated by numerical simulation and phantom experiment. The results verify that the reconstruction with the proposed method is more advantageous, as obvious improvement is observed in the reconstructed image. Conclusions: With the proposed method, the artifact can be effectively suppressed and the reconstructed image of conductivity distribution is improved. It has great potential in medical imaging, which would be helpful for the accurate diagnosis of disease.

Electrical Impedance Tomography for Robot-Aided Internal Radiation Therapy

High dose rate brachytherapy (HDR) is an internal based radiation treatment for prostate cancer. The treatment can deliver radiation to the site of dominant tumor growth within the prostate. Imaging methods to delineate the dominant tumor are imperative to ensure the maximum success of HDR. This paper investigates the feasibility of using electrical impedance tomography (EIT) as the main imaging modality during robot-aided internal radiation therapy. A procedure utilizing brachytherapy needles in order to perform EIT for the purpose of robot-aided prostate cancer imaging is proposed. It is known that cancerous tissue exhibits different conductivity than healthy tissue. Using this information, it is hypothesized that a conductivity map of the tissue can be used to locate and delineate cancerous nodules via EIT. Multiple experiments were conducted using eight brachytherapy needle electrodes. Observations indicate that the imaging procedure is able to observe differences in tissue conductivity in a setting that approximates transperineal HDR and confirm that brachytherapy needles can be used as electrodes for this purpose. The needles can access the tissue at a specific depth that traditional EIT surface electrodes cannot. The results indicate the feasibility of using brachytherapy needles for EIT for the purpose internal radiation therapy.

Sparse image reconstruction of intracerebral hemorrhage with electrical impedance tomography

Purpose: Intracerebral hemorrhage (ICH) is a common disease that is known for its high morbidity, high mortality, and high disability. The fast and accurate detection of ICH is essential for the acute care of patients. Electrical impedance tomography (EIT) offers an alternative with which pathological tissues can be detected by reconstructing conductivity variation. Nevertheless, the sensitive field of EIT is greatly affected by medium distribution, which is referred to as soft-field effect. In addition, the image reconstruction is a severely ill-posed inverse problem. Furthermore, due to the low conductivity of skull, the sensitivity in the sensing area is extremely low. Therefore, the reconstruction of ICH with EIT is great challenge. Approach: A sparse image reconstruction method is proposed for EIT to visualize the conductivity variation caused by ICH. To reduce the impact of soft-field effect, the normalization of sensitivity distribution is conducted for monolayer and three-layer head model. In addition, a constrained sparse L 1 -norm minimization model is developed for the image reconstruction. Augmented Lagrangian multiplier method and alternating minimization scheme are adopted to solve the proposed model. Results: The results show that the sensitivity in the sensing area is largely enhanced. Numerical simulation based on monolayer head model and three-layer head model is respectively carried out. Both the reconstructed images and the quantitative evaluations show that image reconstructed by the proposed method is much better than that reconstructed by traditional Tikhonov method. The reconstructions evaluated under the impact of noise also show that the proposed method has superior anti-noise performance. Conclusions: With the proposed method, the quality of the reconstructed image would be greatly improved. It is an effective approach for imaging ICH with EIT technique.

Phantom Studies of Fused-Data TREIT Using Only Biopsy-Probe Electrodes

Transrectal electrical impedance tomography (TREIT) is a novel imaging modality being developed for prostate biopsy guidance and cancer characterization. We describe a novel fused-data TREIT (fd-TREIT) system and approach developed to improve imaging robustness and evaluate it on challenging clinically-representative phantoms. The new approach incorporates 8 electrodes (in 2 rows) on a biopsy probe (BP) and 12 electrodes on the face of a transrectal ultrasound (TRUS) probe and includes a biopsy gun, instrument tracking, 3D-printed needle guide, and EIT hardware and software. The approach was evaluated via simulation, a series of prostate-shaped gel phantoms, and an ex vivo bovine tissue sample using only absolute reconstructions. The simulations surprisingly found that using only biopsy-probe electrode measurements, i.e. omitting TRUS-probe electrode measurements, significantly improves robustness to noise thus leading to simpler modeling and significant decreases in computational times (~13x speed-up/reconstructions in ~27 minutes). The gel phantom experiments resulted in reconstructions with area under the curve (AUC) values extracted from receiver operator characteristic curves of >0.85 for 4 out of the 5 tests, and when incorporating inclusion boundaries resulted in absolute reconstructions yielding 1.9% and 12.2% average percent errors for 3 consistent tests and all 5 tests, respectively. Ex vivo bovine tests revealed qualitatively that the fd-TREIT approach can largely discriminate a complex adipose and muscle interface in a realistic setting using data from 9 biopsy probe states (biopsy core locations). The algorithms developed here on challenging phantoms suggest strong promise for this technology to aid in imaging during routine 12-core biopsies.

A 1 MHz Miniaturized Electrical Impedance Tomography System for Prostate Imaging

An ASIC for a high frequency electrical impedance tomography (EIT) imaging system for prostate cancer screening is presented. The ASIC enables a small form-factor architecture, which ensures high signal-to-noise ratio (SNR) at MHz frequencies. The 4-channel ASIC was designed and fabricated in a standard CMOS 0.18- μm technology and integrates a novel current driver for current stimulus, instrumentation amplifier to interface with the tissue, VGA to provide variable gain and ADC with SPI interface for digitization. A prototype miniaturized EIT system was built and it was evaluated using a model transrectal imaging probe immersed into a tank filled with saline and a metal inclusion that demonstrated the open-domain problem of imaging prostate cancer lesion. The system maintained an SNR between 66 and 76 dB over the frequency range of 500 Hz to 1 MHz. Also, it produced reconstructed EIT images that depicted the presence of the small metal inclusion that modeled a prostate cancer imaging application.

B-Spline Level Set Method for Shape Reconstruction in Electrical Impedance Tomography

A B-spline level set (BLS) based method is proposed for shape reconstruction in electrical impedance tomography (EIT). We assume that the conductivity distribution to be reconstructed is piecewise constant, transforming the image reconstruction problem into a shape reconstruction problem. The shape/interface of inclusions is implicitly represented by a level set function (LSF), which is modeled as a continuous parametric function expressed using B-spline functions. Starting from modeling the conductivity distribution with the B-spline based LSF, we show that the shape modeling allows us to compute the solution by restricting the minimization problem to the space spanned by the B-splines. As a consequence, the solution to the minimization problem is obtained in terms of the B-spline coefficients. We illustrate the behavior of this method using simulated as well as water tank data. In addition, robustness studies considering varying initial guesses, differing numbers of control points, and modeling errors caused by inhomogeneity are performed. Both simulation and experimental results show that the BLS-based approach offers clear improvements in preserving the sharp features of the inclusions in comparison to the recently published parametric level set method.

A robust and novel electrical impedance metric of pulmonary function in ALS patients

OBJECTIVE

Pulmonary function tests (PFTs) are important for assessing respiratory function in amyotrophic lateral sclerosis (ALS) patients. However, weakness of oral and glottal closure, due to concomitant bulbar dysfunction, may result in unreliable PFT values stemming from leakage of air around the breathing tube and through the glottis. In this study, we assessed whether standard thoracic electrical impedance tomography (EIT) could serve as a surrogate measure for PFTs.

APPROACH

Thoracic EIT was performed simultaneously with standard PFTs on seven ALS patients without clinical bulbar weakness (six men and one woman, mean age of 63 years) and ten healthy volunteers (seven men and three women, mean age of 57 years). A raw impedance metric along with more standard EIT measures were computed and correlated with the normalized forced vital capacity (FVC). Additionally, test/re-test metrics and EIT images were analyzed.

MAIN RESULTS

The impedance metric was found to be robust and sensitive to lung activity. We also identified qualitative EIT differences between healthy volunteers and ALS patients, with the ALS images showing greater heterogeneity. Significant correlations with FVC were found for both impedance and EIT metrics in ALS patients (r² = 0.89) and for the impedance metric only in healthy volunteers (r² = 0.49).

SIGNIFICANCE

This suggests that EIT, using our novel impedance metric, has the potential to serve as an alternative technology to standard PFTs for assessing pulmonary function in patients with ALS, offering new metrics of disease status for those with bulbar weakness.

Induced-Current Learning Method for Nonlinear Reconstructions in Electrical Impedance Tomography

Electrical impedance tomography (EIT) is an attractive technique that aims to reconstruct the unknown electrical property in a domain from the surface electrical measurements. In this work, the induced-current learning method (ICLM) is proposed to solve nonlinear electrical impedance tomography (EIT) problems. Specifically, the cascaded end-to-end convolutional neural network (CEE-CNN) architecture is designed to implement the ICLM. The CEE-CNN greatly decreases the nonlinearities in EIT problems by designing a combined objective function and introducing multiple labels. A noticeable characteristic of the proposed CNN scheme is that the input parameters are chosen as both induced contrast current (ICC) and the updated electrical field from a spectral analysis and the output is chosen as ICC, which is fundamentally different from prevailing CNN schemes. Further, several skip connections are introduced to focus on learning only the unknown part of ICC. ICLM is verified with both numerical and experimental tests on typical EIT problems, and it is found that ICLM is able to solve typical EIT problems in less than 1 second with high image qualities. More importantly, it is also highly robust to measurement noises and modeling errors, such as inaccurate boundary data.

A Moving Morphable Components Based Shape Reconstruction Framework for Electrical Impedance Tomography

This paper presents a new computational framework in electrical impedance tomography (EIT) for shape reconstruction based on the concept of moving morphable components (MMC). In the proposed framework, the shape reconstruction problem is solved in an explicit and geometrical way. Compared with the traditional pixel or shape-based solution framework, the proposed framework can incorporate more geometry and prior information into shape and topology optimization directly and therefore render the solution process more flexibility. It also has the afford potential to substantially reduce the computational burden associated with shape and topology optimization. The effectiveness of the proposed approach is tested with noisy synthetic data and experimental data, which demonstrates the most popular biomedical application of EIT: lung imaging. In addition, robustness studies of the proposed approach considering modeling errors caused by non-homogeneous background, varying initial guesses, differing numbers of candidate shape components, and differing exponent in the shape and topology description function are performed. The simulation and experimental results show that the proposed approach is tolerant to modeling errors and is fairly robust to these parameter choices, offering significant improvements in image quality in comparison to the conventional absolute reconstructions using smoothness prior regularization and total variation regularization.

A Statistical Shape-Constrained Reconstruction Framework for Electrical Impedance Tomography

A statistical shape-constrained reconstruction (SSCR) framework is presented to incorporate the statistical prior information of human lung shapes for lung electrical impedance tomography. The prior information is extracted from 8000 chest-computed tomography scans across 800 patients. The reconstruction framework is implemented with two approaches-a one-step SSCR and an iterative SSCR in lung imaging. The one-step SSCR provides fast and high accurate reconstructions of healthy lungs, whereas the iterative SSCR allows to simultaneously estimate the pre-injured lung and the injury lung part. The approaches are evaluated with the simulated examples of thorax imaging and also with the experimental data from a laboratory setting, with difference imaging considered in both the approaches. It is demonstrated that the accuracy of lung shape reconstruction is significantly improved. In addition, the proposed approaches are proved to be robust against measurement noise, modeling error caused by inaccurately known domain boundary, and the selection of the regularization parameters.

Electrical impedance tomography in perioperative medicine: careful respiratory monitoring for tailored interventions

BACKGROUND

Electrical impedance tomography (EIT) is a non-invasive radiation-free monitoring technique that provides images based on tissue electrical conductivity of the chest. Several investigations applied EIT in the context of perioperative medicine, which is not confined to the intraoperative period but begins with the preoperative assessment and extends to postoperative follow-up.

MAIN BODY

EIT could provide careful respiratory monitoring in the preoperative assessment to improve preparation for surgery, during anaesthesia to guide optimal ventilation strategies and to monitor the hemodynamic status and in the postoperative period for early detection of respiratory complications. Moreover, EIT could further enhance care of patients undergoing perioperative diagnostic procedures. This narrative review summarizes the latest evidence on the application of this technique to the surgical patient, focusing also on possible future perspectives.

CONCLUSIONS

EIT is a promising technique for the perioperative assessment of surgical patients, providing tailored adaptive respiratory and haemodynamic monitoring. Further studies are needed to address the current technological limitations, confirm the findings and evaluate which patients can benefit more from this technology.

A Lagrange-Newton Method for EIT/UT Dual-Modality Image Reconstruction

An image reconstruction method is proposed based on Lagrange-Newton method for electrical impedance tomography (EIT) and ultrasound tomography (UT) dual-modality imaging. Since the change in conductivity distribution is usually accompanied with the change in acoustic impedance distribution, the reconstruction targets of EIT and UT are unified to the conductivity difference using the same mesh model. Some background medium distribution information obtained from ultrasound transmission and reflection measurements can be used to construct a hard constraint about the conductivity difference distribution. Then, the EIT/UT dual-modality inverse problem is constructed by an equality constraint equation, and the Lagrange multiplier method combining Newton-Raphson iteration is used to solve the EIT/UT dual-modality inverse problem. The numerical and experimental results show that the proposed dual-modality image reconstruction method has a better performance than the single-modality EIT method and is more robust to the measurement noise.

Current-conveyor-based wide-band current driver for electrical impedance tomography

OBJECTIVE

In this paper a wide-band integrated current driver for electrical impedance tomography (EIT) is presented. The application is primarily for prostate and breast cancer detection which require the tissue to be interrogated at frequencies up to 10 MHz while achieving low harmonic distortion and high accuracy.

APPROACH

The current driver is based on current conveyor architecture and can deliver 1.2 mA of peak to peak ac current between frequencies of 100 Hz-10 MHz. It is fabricated in CMOS 0.18 [Formula: see text]m technology with a power supply of 3.3 V, and occupies a core area of 0.26 [Formula: see text].

MAIN RESULTS

The measured harmonic distortion for a peak current of 1.2 mA is  <[Formula: see text] for frequencies less than 100 kHz, and increases to [Formula: see text] at 10 MHz. The measured output impedance of the current driver is 101 k[Formula: see text] at 1 MHz and 19.5 k[Formula: see text] at 10 MHz.

SIGNIFICANCE

The circuit is suitable for high frequency active electrode applications.

A Parametric Level Set-Based Approach to Difference Imaging in Electrical Impedance Tomography

This paper presents a novel difference imaging approach based on the recently developed parametric level set (PLS) method for estimating the change in a target conductivity from electrical impedance tomography measurements. As in conventional difference imaging, the reconstruction of conductivity change is based on data sets measured from the surface of a body before and after the change. The key feature of the proposed approach is that the conductivity change to be reconstructed is assumed to be piecewise constant, while the geometry of the anomaly is represented by a shape-based PLS function employing Gaussian radial basis functions (GRBFs). The representation of the PLS function by using GRBF provides flexibility in describing a large class of shapes with fewer unknowns. This feature is advantageous, as it may significantly reduce the overall number of unknowns, improve the condition number of the inverse problem, and enhance the computational efficiency of the technique. To evaluate the proposed PLS-based difference imaging approach, results obtained via simulation, phantom study, and in vivo pig data are studied. We find that the proposed approach tolerates more modeling errors and leads to a significant improvement in image quality compared with the conventional linear approach.

Fused-data transrectal EIT for prostate cancer imaging

OBJECTIVE

Prostate cancer is a significant problem affecting 1 in 7 men. Unfortunately, the diagnostic gold-standard of ultrasound-guided biopsy misses 10%-30% of all cancers. The objective of this study was to develop an electrical impedance tomography (EIT) approach that has the potential to image the entire prostate using multiple impedance measurements recorded between electrodes integrated onto an end-fired transrectal ultrasound (TRUS) device and a biopsy probe (BP).

APPROACH

Simulations and sensitivity analyses were used to investigate the best combination of electrodes, and measured tank experiments were used to evaluate a fused-data transrectal EIT (fd-TREIT) and BP approach.

MAIN RESULTS

Simulations and sensitivity analysis revealed that (1) TREIT measurements are not sufficiently sensitive to image the whole prostate, (2) the combination of TREIT  +  BP measurements increases the sensitive region of TREIT-only measurements by 12×, and (3) the fusion of multiple TREIT  +  BP measurements collected during a routine or customized 12-core biopsy procedure can cover up to 76.1% or 94.1% of a nominal 50 cm³ prostate, respectively. Three measured tank experiments of the fd-TREIT  +  BP approach successfully and accurately recovered the positions of 2-3 metal or plastic inclusions.

SIGNIFICANCE

The measured tank experiments represent important steps in the development of an algorithm that can combine EIT from multiple locations and from multiple probes-data that could be collected during a routine TRUS-guided 12-core biopsy. Overall, this result is a step towards a clinically deployable impedance imaging approach to scanning the entire prostate, which could significantly help to improve prostate cancer diagnosis.

A Parametric Level Set Method for Electrical Impedance Tomography

This paper presents an image reconstruction method based on parametric level set (PLS) method using electrical impedance tomography. The conductivity to be reconstructed was assumed to be piecewise constant and the geometry of the anomaly was represented by a shape-based PLS function, which we represent using Gaussian radial basis functions (GRBF). The representation of the PLS function significantly reduces the number of unknowns, and circumvents many difficulties that are associated with traditional level set (TLS) methods, such as regularization, re-initialization and use of signed distance function. PLS reconstruction results shown in this article are some of the first ones using experimental EIT data. The performance of the PLS method was tested with water tank data for two-phase visualization and with simulations which demonstrate the most popular biomedical application of EIT: lung imaging. In addition, robustness studies of the PLS method w.r.t width of the Gaussian function and GRBF centers were performed on simulated lung imaging data. The experimental and simulation results show that PLS method has significant improvement in image quality compared with the TLS reconstruction.