Deep D-Bar: Real-Time Electrical Impedance Tomography Imaging With Deep Neural Networks

The mathematical problem for electrical impedance tomography (EIT) is a highly nonlinear ill-posed inverse problem requiring carefully designed reconstruction procedures to ensure reliable image generation. D-bar methods are based on a rigorous mathematical analysis and provide robust direct reconstructions by using a low-pass filtering of the associated nonlinear Fourier data. Similarly to low-pass filtering of linear Fourier data, only using low frequencies in the image recovery process results in blurred images lacking sharp features, such as clear organ boundaries. Convolutional neural networks provide a powerful framework for post-processing such convolved direct reconstructions. In this paper, we demonstrate that these CNN techniques lead to sharp and reliable reconstructions even for the highly nonlinear inverse problem of EIT. The network is trained on data sets of simulated examples and then applied to experimental data without the need to perform an additional transfer training. Results for absolute EIT images are presented using experimental EIT data from the ACT4 and KIT4 EIT systems

DICOM for EIT

With EIT starting to be used in routine clinical practice [1], it important that the clinically relevant information is portable between hospital data management systems. DICOM formats are widely used clinically and cover many imaging modalities, though not specifically EIT. We describe how existing DICOM specifications, can be repurposed as an interim solution, and basis from which a consensus EIT DICOM ‘Supplement’ (an extension to the standard) can be writte

Estimation of thorax shape for forward modelling in lungs EIT

The thorax models for pre-term babies are developed based on the CT scans from new-borns and their effect on image reconstruction is evaluated in comparison with other available models

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

Torso shape detection to improve lung monitoring

Two methodologies are proposed to detect the patient-specific boundary of the chest, aiming to produce a more accurate forward model for EIT analysis. Thus, a passive resistive and an inertial prototypes were prepared to characterize and reconstruct the shape of multiple phantoms. Preliminary results show how the passive device generates a minimum scatter between the reconstructed image and the actual shap

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

Nanoparticle electrical impedance tomography

We have developed a new approach to imaging with electrical impedance tomography (EIT) using gold nanoparticles (AuNPs) to enhance impedance changes at targeted tissue sites. This is achieved using radio frequency (RF) to heat nanoparticles while applying EIT imaging. The initial results using 5-nm citrate coated AuNPs show that heating can enhance the impedance in a solution containing AuNPs due to the application of an RF field at 2.60 GHz

Patient-Specific 3D Printed Models for Education, Research and Surgical Simulation

3D printing techniques are increasingly used in engineering science, allowing the use of computer aided design (CAD) to rapidly and inexpensively create prototypes and components. There is also growing interest in the application of these techniques in a clinical context for the creation of anatomically accurate 3D printed models from medical images for therapy planning, research, training and teaching applications. However, the techniques and tools available to create 3D models of anatomical structures typically require specialist knowledge in image processing and mesh manipulation to achieve. In this book chapter we describe the advantages of 3D printing for patient education, healthcare professional education, interventional planning and implant development. We also describe how to use medical image data to segment volumes of interest, refine and prepare for 3D printing. We will use a lung as an example. The information in this section will allow anyone to create own 3D printed models from medical image data. This knowledge will be of use to anyone with little or no previous experience in medical image processing who have identified a potential application for 3D printing in a medical context, or those with a more general interest in the techniques

Effects of patient recumbency position on neonatal chest EIT

This paper investigates the overlooked effects of the patient recumbency positions on one of the key clinically used parameters in chest electrical impedance tomography (EIT) monitoring – the silent spaces. This parameter could impact medical decisions and interventions by indicating how well each lung is being ventilated. Yet it is largely dependent on assumptions of prior model at the reconstruction stage and the closely linked region of interest (ROI) during the final calculations. The potential effect of switching recumbency modes on silent spaces as a results of internal organ movements and consequently changes in initial assumptions, has been studied. The displacement and deformations caused by posture changes from supine to lateral recumbent were evaluated via simulations considering the simultaneous gravity-dependent movement and/or deformations of heart, mediastinum, lungs and the diaphragm. The reliability of simulations was verified against reference radiography images of an 18-month-old infant in supine and decubitus lateral positions. Inspecting a set of 10 patients from age range of 1 to 2 years old revealed improvements of up to 30% in the silent space parameters when applying posture consistent amendments as opposed to fixed model/ROI to each individual. To minimize the influence of image reconstruction technique on the results two different EIT reconstruction algorithms were implemented. The outcome emphasized the importance of including recumbency situation during chest EIT monitoring within the considered age range

Effects of Patient Recumbency Position on Neonatal Chest EIT

This paper investigates the overlooked effects of the patient recumbency positions on one of the key clinically used parameters in chest electrical impedance tomography (EIT) monitoring – the silent spaces. This parameter could impact medical decisions and interventions by indicating how well each lung is being ventilated. Yet it is largely dependent on assumptions of prior model at the reconstruction stage and the closely linked region of interest (ROI) during the final calculations. The potential effect of switching recumbency modes on silent spaces as a results of internal organ movements and consequently changes in initial assumptions, has been studied. The displacement and deformations caused by posture changes from supine to lateral recumbent were evaluated via simulations considering the simultaneous gravity-dependent movement and/or deformations of heart, mediastinum, lungs and the diaphragm. The reliability of simulations was verified against reference radiography images of an 18-month-old infant in supine and decubitus lateral positions. Inspecting a set of 10 patients from age range of 1 to 2 years old revealed improvements of up to 30% in the silent space parameters when applying posture consistent amendments as opposed to fixed model/ROI to each individual. To minimize the influence of image reconstruction technique on the results two different EIT reconstruction algorithms were implemented. The outcome emphasized the importance of including recumbency situation during chest EIT monitoring within the considered age range