Sensitivity Analysis Highlights the Importance of Accurate Head Models for Electrical Impedance Tomography Monitoring of Intracerebral Hemorrhagic Stroke

Biomechanical simulations of intracerebral hemorrhage expansion show tissue displacement has significant impact on electrical impedance tomography results

OBJECTIVE

Intracerebral hemorrhage (ICH) occupies intracranial space and causes brain tissue displacement and fluid volume shifts. We assess how hematoma expansion (HE) affects electrical impedance tomography (EIT) measurements and reconstructed images of the conductivity change caused by HE.

METHODS

We developed a novel multi-physics model of ICH with mechanical tissue deformation during HE. We simulated EIT measurements with the multi-physics model and a traditional static model using five ICH locations. The effects of tissue deformation on the results of monitoring of ICH with EIT were assessed by comparing the measurement data from the multi-physics and traditional models and by comparing the corresponding reconstructed conductivity change from two image reconstruction algorithms.

RESULTS

The simulated measurement data and the reconstructed images of the conductivity change using the multi-physics and the traditional model are radically different regardless of the image reconstruction algorithm used.

CONCLUSIONS

The effect of tissue displacement caused by HE on EIT monitoring of ICH is significant. Specifically, the displacement of cerebrospinal fluid (CSF) can mask the effects of increased ICH blood volume. However, the effects of displaced CSF could be easier to detect with EIT than the ICH blood volume increase and thus could be used as an indicator of HE in EIT bedside monitoring of ICH and improve the detectability of HE, especially for ICH located deep in the brain.

SIGNIFICANCE

Currently there are virtually no imaging methods for continuous monitoring of stroke. There has been recent resurgence in interest to develop electrical impedance tomography (EIT) devices and algorithms for monitoring progression of stroke. In-silico studies show promising results, but there are very little clinical results. In-silico models are usually used for development and evaluation of algorithms for EIT image reconstruction. In previous studies the stroke has been usually modeled as local change in electrical conductivity and the fluid and tissue displacement caused by the increased blood volume in ICH has not been considered. In this paper we present a novel multi-physics model of ICH, simulated EIT measurement results and reconstructed images with comparisons to the traditionally used ICH modeling methods. Our multi-physics approach to modeling of ICH shows that the effect of tissue and fluid displacement during HE needs consideration when developing clinical applications of EIT.

Sensitivity volume as figure-of-merit for maximizing data importance in electrical impedance tomography

Objective.Electrical impedance tomography (EIT) is a noninvasive imaging method whereby electrical measurements on the periphery of a heterogeneous conductor are inverted to map its internal conductivity. The EIT method proposed here aims to improve computational speed and noise tolerance by introducing sensitivity volume as a figure-of-merit for comparing EIT measurement protocols.Approach.Each measurement is shown to correspond to a sensitivity vector in model space, such that the set of measurements, in turn, corresponds to a set of vectors that subtend a sensitivity volume in model space. A maximal sensitivity volume identifies the measurement protocol with the greatest sensitivity and greatest mutual orthogonality. A distinguishability criterion is generalized to quantify the increased noise tolerance of high sensitivity measurements.Main result.The sensitivity volume method allows the model space dimension to be minimized to match that of the data space, and the data importance to be increased within an expanded space of measurements defined by an increased number of contacts.Significance.The reduction in model space dimension is shown to increasecomputational efficiency, accelerating tomographic inversion by several orders of magnitude, while the enhanced sensitivitytolerates higher noiselevels up to several orders of magnitude larger than standard methods.

Applied machine learning for stroke differentiation by electrical impedance tomography with realistic numerical models

Electrical impedance tomography (EIT) may have potential to overcome existing limitations in stroke differentiation, enabling low-cost, rapid, and mobile data collection. Combining bioimpedance measurement technologies such as EIT with machine learning classifiers to support decision-making can avoid commonly faced reconstruction challenges due to the nonlinear and ill-posed nature of EIT imaging. Therefore, in this work, we advance this field through a study integrating realistic head models with clinically relevant test scenarios, and a robust architecture consisting of nested cross-validation and principal component analysis. Specifically, realistic head models are designed which incorporate the highly conductive layers of cerebrospinal fluid in the subarachnoid space and ventricles. In total, 135 unique models are created to represent a large patient population, with normal, haemorrhagic, and ischemic brains. Simulated EIT voltage data generated from these models are used to assess the classification performance of support vector machines. Parameters explored include driving frequency, signal-to-noise ratio, kernel function, and composition of binary classes. Classifier accuracy at 60 dB signal-to-noise ratio, reported as mean and standard deviation, are (79.92% ± 10.82%) for lesion differentiation, (74.78% ± 3.79%) for lesion detection, (77.49% ± 15.90%) for bleed detection, and (60.31% ± 3.98%) for ischemia detection (after ruling out bleed). The results for each method were obtained with statistics from 3 independent runs with 17,280 observations, polynomial kernel functions, and feature reduction of 76% by PCA (from 208 to 50 features). While results of this study show promise for stroke differentiation using EIT data, our findings indicate that the achievable accuracy is highly dependent on the classification scenario and application-specific classifiers may be necessary to achieve acceptable accuracy.

Fast Iterative Shrinkage-Thresholding Algorithm with Continuation for Brain Injury Monitoring Imaging Based on Electrical Impedance Tomography

Electrical impedance tomography (EIT) is low-cost and noninvasive and has the potential for real-time imaging and bedside monitoring of brain injury. However, brain injury monitoring by EIT imaging suffers from image noise (IN) and resolution problems, causing blurred reconstructions. To address these problems, a least absolute shrinkage and selection operator model is built, and a fast iterative shrinkage-thresholding algorithm with continuation (FISTA-C) is proposed. Results of numerical simulations and head phantom experiments indicate that FISTA-C reduces IN by 63.2%, 47.2%, and 29.9% and 54.4%, 44.7%, and 22.7%, respectively, when compared with the damped least-squares algorithm, the split Bergman, and the FISTA algorithms. When the signal-to-noise ratio of the measurements is 80-50 dB, FISTA-C can reduce IN by 83.3%, 72.3%, and 68.7% on average when compared with the three algorithms, respectively. Both simulation and phantom experiments suggest that FISTA-C produces the best image resolution and can identify the two closest targets. Moreover, FISTA-C is more practical for clinical application because it does not require excessive parameter adjustments. This technology can provide better reconstruction performance and significantly outperforms the traditional algorithms in terms of IN and resolution and is expected to offer a general algorithm for brain injury monitoring imaging via EIT.

Sensitivity Analysis of Circular and Helmet Coil Arrays in Magnetic Induction Tomography for Stroke Detection

Magnetic induction tomography (MIT) is harmless and contactless technique for measuring the conductivity of the biological tissue. MIT could be used for initial diagnosis and continuous monitoring of stroke. Different kinds of coil arrays have been proposed for MIT systems. Previous research results using a circular 16-channel MIT model reported difficulties with detection and measurement of small bioelectric signals. For stroke imaging, a system with a higher sensitivity is required. We aim to improve the sensitivity by increasing the number of coils and placing them closer to the head. In this paper, a helmet type coil array with 31 coils is introduced. For simplicity, the head is modelled as a sphere with white matter as a material. The stroke is simulated as a single inclusion with blood and assigned different sizes and positions. Sensitivity distribution and target response of the stroke were evaluated for the helmet model and compared with the circular MIT system. The simulations and analysis were performed at 10 MHz frequency with different coil pairs. Results from comparison of the two MIT models show that the Helmet coil array provides better spatial sensitivity, which has been estimated to be more than 20 times higher than the circular model. Further, when all coils are taken in account, the recorded sensitivity improvement was in the range of 13-90-fold.

Corrections to "Sensitivity Analysis Highlights the Importance of Accurate Head Models for Electrical Impedance Tomography Monitoring of Intracerebral Hemorrhagic Stroke"

In the above paper [1] there are two errors which we correct here. An important reference was omitted.