We investigate the application of multifrequency electrical impedance tomography (MFEIT) to imaging the brain in stroke patients. The use of MFEIT could enable early diagnosis and thrombolysis of ischaemic stroke, and therefore improve the outcome of treatment. Recent advances in the imaging methodology suggest that the use of spectral constraints could allow for the reconstruction of a one-shot image. We performed a simulation study to investigate the feasibility of imaging stroke in a head model with realistic conductivities. We introduced increasing levels of modelling errors to test the robustness of the method to the most common sources of artefact. We considered the case of errors in the electrode placement, spectral constraints, and contact impedance. The results indicate that errors in the position and shape of the electrodes can affect image quality, although our imaging method was successful in identifying tissues with sufficiently distinct spectra.

Electrical impedance tomography (EIT) is a noninvasive imaging modality, where imperceptible currents are applied to the skin and the resulting surface voltages are measured. It has the potential to distinguish between ischaemic and haemorrhagic stroke with a portable and inexpensive device. The image reconstruction relies on an accurate forward model of the experimental setup. Because of the relatively small signal in stroke EIT, the finite-element modeling requires meshes of more than 10 million elements. To study the requirements in the forward modeling in EIT and also to reduce the time for experimental image acquisition, it is necessary to reduce the run time of the forward computation. We show the implementation of a parallel forward solver for EIT using the Dune-Fem C++ library and demonstrate its performance on many CPU's of a computer cluster. For a typical EIT application a direct solver was significantly slower and not an alternative to iterative solvers with multigrid preconditioning. With this new solver, we can compute the forward solutions and the Jacobian matrix of a typical EIT application with 30 electrodes on a 15-million element mesh in less than 15 min. This makes it a valuable tool for simulation studies and EIT applications with high precision requirements. It is freely available for download.

G␤␥ subunits modulate several distinct molecular events involved with G protein signaling. In addition to regulating several effector proteins, G␤␥ subunits help anchor G␣ subunits to the plasma membrane, promote interaction of G␣ with receptors, stabilize the binding of GDP to G␣ to suppress spurious activation, and provide membrane contact points for G protein-coupled receptor kinases. G␤␥ subunits have also been shown to inhibit the activities of GTPase-activating proteins (GAPs), both phospholipase C (PLC)-␤s and RGS proteins, when assayed in solution under single turnover conditions. We show here that G␤␥ subunits inhibit G protein GAP activity during receptor-stimulated, steady-state GTPase turnover. GDP/GTP exchange catalyzed by receptor requires G␤␥ in amounts approximately equimolar to G␣, but GAP inhibition was observed with superstoichiometric G␤␥. The potency of inhibition varied with the GAP and the G␣ subunit, but half-maximal inhibition of the GAP activity of PLC-␤1 was observed with 5-10 nM G␤␥, which is at or below the concentrations of G␤␥ needed for regulation of physiologically relevant effector proteins. The kinetics of GAP inhibition of both receptor-stimulated GTPase activity and single turnover, solution-based GAP assays suggested a competitive mechanism in which G␤␥ competes with GAPs for binding to the activated, GTP-bound G␣ subunit. An N-terminal truncation mutant of PLC-␤1 that cannot be directly regulated by G␤␥ remained sensitive to inhibition of its GAP activity, suggesting that the G␤␥ binding site relevant for GAP inhibition is on the G␣ subunit rather than on the GAP. Using fluorescence resonance energy transfer between cyan or yellow fluorescent protein-labeled G protein subunits and Alexa532-labeled RGS4, we found that G␤␥ directly competes with RGS4 for high-affinity binding to G␣ i -GDP-AlF 4 .G␤␥ subunits perform diverse roles in G protein-mediated signaling, almost all of which are based on their regulated binding to G␣. G␤␥ binds most tightly to the GDP-bound form of G␣, usually considered the inactive conformation. Because G␤␥ and GDP bind positively cooperatively to G␣, G␤␥ stabilizes GDP binding and thus suppresses spontaneous G␣ activation. Conversely, GDP stabilizes G␤␥ binding and suppresses its ability to spontaneously regulate effector proteins. In contrast, G␤␥ binds least tightly to the GTP-bound, activated conformation of G␣. Such negative cooperative binding permits G␤␥ to regulate effectors during G protein activation. In solution, activation of G␣ by a nonhydrolyzable GTP analog can drive physical dissociation of G␤␥ (1-4) (see Refs. 5 and 6 for reviews). Complete dissociation of G␤␥ from G␣-GTP also occurs to some extent in biological membranes during receptor-initiated activation of G␣ (7,8). However, recent data suggest that G␤␥ bound to G␣-GTP can also interact productively with effector proteins without dissociating from the heterotrimer (8 -11). This implies that the G␣-GTP-G␤␥ complex somehow exposes the sites on G␣ and G␤␥ necessary for productive i...

Electrical impedance tomography (EIT) is a promising medical imaging technique which could aid differentiation of haemorrhagic from ischaemic stroke in an ambulance. One challenge in EIT is the ill-posed nature of the image reconstruction, i.e., that small measurement or modelling errors can result in large image artefacts. It is therefore important that reconstruction algorithms are improved with regard to stability to modelling errors. We identify that wrongly modelled electrode positions constitute one of the biggest sources of image artefacts in head EIT. Therefore, the use of the Fréchet derivative on the electrode boundaries in a realistic three-dimensional head model is investigated, in order to reconstruct electrode movements simultaneously to conductivity changes. We show a fast implementation and analyse the performance of electrode position reconstructions in time-difference and absolute imaging for simulated and experimental voltages. Reconstructing the electrode positions and conductivities simultaneously increased the image quality significantly in the presence of electrode movement.

Head imaging with electrical impedance tomography (EIT) is usually done with time-differential measurements, to reduce time-invariant modelling errors. Previous research suggested that more accurate head models improved image quality, but no thorough analysis has been done on the required accuracy. We propose a novel pipeline for creation of precise head meshes from magnetic resonance imaging and computed tomography scans, which was applied to four different heads. Voltages were simulated on all four heads for perturbations of different magnitude, haemorrhage and ischaemia, in five different positions and for three levels of instrumentation noise. Statistical analysis showed that reconstructions on the correct mesh were on average 25% better than on the other meshes. However, the stroke detection rates were not improved. We conclude that a generic head mesh is sufficient for monitoring patients for secondary strokes following head trauma.

Electrical impedance tomography (EIT) or electrical resistivity tomography (ERT) current and measure voltages at the boundary of a domain through electrodes. Significance: The movement or incorrect placement of electrodes may lead to modelling errors that result in significant reconstructed image artifacts. These errors may be accounted for by allowing for electrode position estimates in the model. Movement may be reconstructed through a firstorder approximation, the electrode position Jacobian. A reconstruction that incorporates electrode position estimates and conductivity can significantly reduce image artifacts. Conversely, if electrode position is ignored it can be difficult to distinguish true conductivity changes from reconstruction artifacts which may increase the risk of a flawed interpretation. Objective: In this work, we aim to determine the fastest, most accurate approach for estimating the electrode position Jacobian. Approach: Four methods of calculating the electrode position Jacobian were evaluated on a homogeneous halfspace. Main results: Results show that Fréchet derivative and rank-one update methods are competitive in computational efficiency but achieve different solutions for certain values of contact impedance and mesh density.

We conclude that for fast neural EIT applications, the protocol that maximises current density is the best protocol to implement.

The differentiation of haemorrhagic from ischaemic stroke using electrical impedance tomography (EIT) requires measurements at multiple frequencies, since the general lack of healthy measurements on the same patient excludes time-difference imaging methods. It has previously been shown that the inaccurate modelling of electrodes constitutes one of the largest sources of image artefacts in non-linear multi-frequency EIT applications. To address this issue, we augmented the conductivity Jacobian matrix with a Jacobian matrix with respect to electrode movement. Using this new algorithm, simulated ischaemic and haemorrhagic strokes in a realistic head model were reconstructed for varying degrees of electrode position errors. The simultaneous recovery of conductivity spectra and electrode positions removed most artefacts caused by inaccurately modelled electrodes. Reconstructions were stable for electrode position errors of up to 1.5 mm standard deviation along both surface dimensions. We conclude that this method can be used for electrode model correction in multi-frequency EIT.