Michael Crabb

Mutual information as a measure of image quality for 3D dynamic lung imaging with EIT

We report on a pilot study of dynamic lung electrical impedance tomography (EIT) at the University of Manchester. Low-noise EIT data at 100 frames per second were obtained from healthy male subjects during controlled breathing, followed by magnetic resonance imaging (MRI) subsequently used for spatial validation of the EIT reconstruction. The torso surface in the MR image and electrode positions obtained using MRI fiducial markers informed the construction of a 3D finite element model extruded along the caudal-distal axis of the subject. Small changes in the boundary that occur during respiration were accounted for by incorporating the sensitivity with respect to boundary shape into a robust temporal difference reconstruction algorithm. EIT and MRI images were co-registered using the open source medical imaging software, 3D Slicer. A quantitative comparison of quality of different EIT reconstructions was achieved through calculation of the mutual information with a lung-segmented MR image. EIT reconstructions using a linear shape correction algorithm reduced boundary image artefacts, yielding better contrast of the lungs, and had 10% greater mutual information compared with a standard linear EIT reconstruction.

Convergence study of 2D forward problem of electrical impedance tomography with high-order finite elements

A convergence study of the forward problem of electrical impedance tomography is performed using triangular high-order piecewise polynomial finite-element methods (p-FEM) on a square domain. The computation of p-FEM for the complete electrode model (CEM) is outlined and a novel analytic solution to the CEM on a square domain is presented. Errors as a function of mesh-refinement and computational time, as well as convergence rates as a function of contact impedance, are computed numerically for different polynomial approximation orders. It is demonstrated that p-FEM can generate more accurate forward solutions in less computational time, which implies more accurate simulated interior potentials, electrode voltages and conductivity Jacobians.

Methods for calculating the electrode position Jacobian for impedance imaging

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 first-order 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.

On the feasibility of automated mechanical ventilation control through EIT

Objective: This paper aims to demonstrate the feasibility of coupling electrical impedance tomography (EIT) with models of lung function in order to recover parameters and inform mechanical ventilation control. Methods: A compartmental ordinary differential equation model of lung function is coupled to simulations of EIT, assuming accurate modeling and movement tracking, to generate time series values of bulk conductivity. These values are differentiated and normalized against the total air volume flux to recover regional volumes and flows. These ventilation distributions are used to recover regional resistance and elastance properties of the lung. Linear control theory is used to demonstrate how these parameters may be used to generate a patient-specific pressure mode control. Results: Ventilation distributions are shown to be recoverable, with Euclidean norm errors in air flow below 9% and volume below 3%. The parameters are also shown to be recoverable, although errors are higher for resistance values than elastance. The control constructed is shown to have minimal

Accounting for electrode movement in MRI-informed functional EIT lung imaging

H^1

Improved amplitude estimation of lung EIT signals in the presence of transients: Experimental validation using discrete phantoms

seminorm resulting in bounded magnitudes and minimal gradients. Conclusion: The recovery of regional ventilation distributions and lung parameters is feasible with the use of EIT. These parameters may then be used in model based control schemes to provide patient-specific care. Significance: For pulmonary-intensive-care patients mechanical ventilation is a life saving intervention, requiring careful calibration of pressure settings. Both magnitudes and gradients of pressure can contribute to ventilator induced lung injury. Retrieving regional lung parameters allows the design of patient-specific ventilator controls to reduce injury.