Application of constrained optimisation techniques in electrical impedance tomography

A Constrained Optimisation technique is described for the reconstruction of temporal resistivity images. The approach solves the Inverse problem by optimising a cost function under constraints, in the form of normalised boundary potentials. Mathematical models have been developed for two different data collection methods for the chosen criterion. Both of these models express the reconstructed image in terms of one dimensional (I-D) Lagrange multiplier functions. The reconstruction problem becomes one of estimating these 1-D functions from the normalised boundary potentials. These models are based on a cost criterion of the minimisation of the variance between the reconstructed resistivity distribution and the true resistivity distribution. The methods presented In this research extend the algorithms previously developed for X-ray systems. Computational efficiency is enhanced by exploiting the structure of the associated system matrices. The structure of the system matrices was preserved in the Electrical Impedance Tomography (EIT) implementations by applying a weighting due to non-linear current distribution during the backprojection of the Lagrange multiplier functions. In order to obtain the best possible reconstruction it is important to consider the effects of noise in the boundary data. This is achieved by using a fast algorithm which matches the statistics of the error in the approximate inverse of the associated system matrix with the statistics of the noise error in the boundary data. This yields the optimum solution with the available boundary data. Novel approaches have been developed to produce the Lagrange multiplier functions. Two alternative methods are given for the design of VLSI implementations of hardware accelerators to improve computational efficiencies. These accelerators are designed to implement parallel geometries and are modelled using a verification description language to assess their performance capabilities

16th International Conference on Electrical Bio-impedance and 17th International Conference on Biomedical Applications of Electrical Impedance Tomography (EIT) [Editorial]

Editoria

Rapid generation of subject-specific thorax forward models

For real-time monitoring of lung function using accurate patient geometry, shape information needs to be acquired and a forward model generated rapidly. This paper shows that warping a cylindrical model to an acquired shape results in meshes of acceptable mesh quality, in terms of stretch and aspect ratio

Focus on advances in electrical impedance tomography

Editoria

Medical Physics: forming and testing solutions to clinical problems

According to the European Federation of Organizations for Medical Physics (EFOMP) policy statement No. 13, “The rapid advance in the use of highly sophisticated equipment and procedures in the medical field increasingly depends on information and communication technology. In spite of the fact that the safety and quality of such technology is vigorously tested before it is placed on the market, it often turns out that the safety and quality is not sufficient when used under hospital working conditions. To improve safety and quality for patient and users, additional safeguards and related monitoring, as well as measures to enhance quality, are required. Furthermore a large number of accidents and incidents happen every year in hospitals and as a consequence a number of patients die or are injured. Medical Physicists are well positioned to contribute towards preventing these kinds of events”. The newest developments related to this increasingly important medical speciality were presented during the 8th European Conference of Medical Physics 2014 which was held in Athens, 11–13 September 2014 and hosted by the Hellenic Association of Medical Physicists (HAMP) in collaboration with the EFOMP and are summarized in this issue

EIT2015: promoting electrical impedance tomography as a non-invasive monitoring technology

Editorial. No abstract

Protein misfolding thermodynamics

It is known that protein misfolding is governed by the hydrophobic effect of solutes at hydrophobic amino acid side chains. The hydrophobic force of nonaqueous solutes acts as a driving force for the spatial rearrangement of protein side chains, whose structural transitions need to be regulated in both time and space. Smaller hydrophobic solutes exert more effect at protein side chains, which involves the clustering of proteins into misfolded shapes. The consequences of misfolding are loss of protein function, gain of toxic function, or both. This is a physical process, whose result has been directly linked to a large number of human diseases

Analysis and compensation for errors in electrical impedance tomography images and ventilation-­related measures due to serial data collection

Electrical impedance tomography (EIT) is increasingly being used as a bedside tool for monitoring regional lung ventilation. However, most clinical systems use serial data collection which, if uncorrected, results in image distortion, particularly at high breathing rates. The objective of this study was to determine the extent to which this affects derived parameters. Raw EIT data were acquired with the GOE­MF II EIT device (CareFusion, Höchberg, Germany) at a scan rate of 13 images/s during both spontaneous breathing and mechanical ventilation. Boundary data for periods of undisturbed tidal breathing were corrected for serial data collection errors using a Fourier based algorithm. Images were reconstructed for both the corrected and original data using the GREIT algorithm, and parameters describing the filling characteristics of the right and left lung derived on a breath by breath basis. Values from the original and corrected data were compared using paired t­ tests. Of the 33 data sets, 23 showed significant differences in filling index for at least one region, 11 had significant differences in calculated tidal impedance change and 12 had significantly different filling fractions (p = 0.05). We conclude that serial collection errors should be corrected before image reconstruction to avoid clinically misleading results

A framework for adapting deep brain stimulation using Parkinsonian state estimates

The mechanisms underlying the beneficial effects of deep brain stimulation (DBS) for Parkinson's disease (PD) remain poorly understood and are still under debate. This has hindered the development of adaptive DBS (aDBS). For further progress in aDBS, more insight into the dynamics of PD is needed, which can be obtained using machine learning models. This study presents an approach that uses generative and discriminative machine learning models to more accurately estimate the symptom severity of patients and adjust therapy accordingly. A support vector machine is used as the representative algorithm for discriminative machine learning models, and the Gaussian mixture model is used for the generative models. Therapy is effected using the state estimates obtained from the machine learning models together with a fuzzy controller in a critic-actor control approach. Both machine learning model configurations achieve PD suppression to desired state in 7 out of 9 cases; most of which settle in under 2 s