1,038 research outputs found
A mathematical and numerical framework for ultrasonically-induced Lorentz force electrical impedance tomography
We provide a mathematical analysis and a numerical framework for Lorentz
force electrical conductivity imaging. Ultrasonic vibration of a tissue in the
presence of a static magnetic field induces an electrical current by the
Lorentz force. This current can be detected by electrodes placed around the
tissue; it is proportional to the velocity of the ultrasonic pulse, but depends
nonlinearly on the conductivity distribution. The imaging problem is to
reconstruct the conductivity distribution from measurements of the induced
current. To solve this nonlinear inverse problem, we first make use of a
virtual potential to relate explicitly the current measurements to the
conductivity distribution and the velocity of the ultrasonic pulse. Then, by
applying a Wiener filter to the measured data, we reduce the problem to imaging
the conductivity from an internal electric current density. We first introduce
an optimal control method for solving such a problem. A new direct
reconstruction scheme involving a partial differential equation is then
proposed based on viscosity-type regularization to a transport equation
satisfied by the current density field. We prove that solving such an equation
yields the true conductivity distribution as the regularization parameter
approaches zero. We also test both schemes numerically in the presence of
measurement noise, quantify their stability and resolution, and compare their
performance
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
A fully non-linear optimization approach to acousto-electric tomography
This paper considers the non-linear inverse problem of reconstructing an
electric conductivity distribution from the interior power density in a bounded
domain. Applications include the novel tomographic method known as
acousto-electric tomography, in which the measurement setup in Electrical
Impedance Tomography is modulated by ultrasonic waves thus giving rise to a
method potentially having both high contrast and high resolution. We formulate
the inverse problem as a regularized non-linear optimization problem, show the
existence of a minimizer, and derive optimality conditions. We propose a
non-linear conjugate gradient scheme for finding a minimizer based on the
optimality conditions. All our numerical experiments are done in
two-dimensions. The experiments reveal new insight into the non-linear effects
in the reconstruction. One of the interesting features we observe is that,
depending on the choice of regularization, there is a trade-off between high
resolution and high contrast in the reconstructed images. Our proposed
non-linear optimization framework can be generalized to other hybrid imaging
modalities
The Linearized Inverse Problem in Multifrequency Electrical Impedance Tomography
This paper provides an analysis of the linearized inverse problem in
multifrequency electrical impedance tomography. We consider an isotropic
conductivity distribution with a finite number of unknown inclusions with
different frequency dependence, as is often seen in biological tissues. We
discuss reconstruction methods for both fully known and partially known
spectral profiles, and demonstrate in the latter case the successful employment
of difference imaging. We also study the reconstruction with an imperfectly
known boundary, and show that the multifrequency approach can eliminate
modeling errors and recover almost all inclusions. In addition, we develop an
efficient group sparse recovery algorithm for the robust solution of related
linear inverse problems. Several numerical simulations are presented to
illustrate and validate the approach.Comment: 25 pp, 11 figure
Regularized Shallow Image Prior for Electrical Impedance Tomography
Untrained Neural Network Prior (UNNP) based algorithms have gained increasing
popularity in tomographic imaging, as they offer superior performance compared
to hand-crafted priors and do not require training. UNNP-based methods usually
rely on deep architectures which are known for their excellent feature
extraction ability compared to shallow ones. Contrary to common UNNP-based
approaches, we propose a regularized shallow image prior method that combines
UNNP with hand-crafted prior for Electrical Impedance Tomography (EIT). Our
approach employs a 3-layer Multi-Layer Perceptron (MLP) as the UNNP in
regularizing 2D and 3D EIT inversion. We demonstrate the influence of two
typical hand-crafted regularizations when representing the conductivity
distribution with shallow MLPs. We show considerably improved EIT image quality
compared to conventional regularization algorithms, especially in structure
preservation. The results suggest that combining the shallow image prior and
the hand-crafted regularization can achieve similar performance to the Deep
Image Prior (DIP) but with less architectural dependency and complexity of the
neural network
Tecniche Elettrotomografiche per la caratterizzazione dei tessuti biologici
Electrical impedance tomography (EIT) is an imaging modality wherein the spatial map of conductivity and permittivity inside a medium is obtained from a set of surface electrical measurements. Electrodes are brought into contact with the surface of the object being imaged and a set of currents are applied and the corresponding voltages are measured. These voltages and currents are then used to estimate the electrical properties of the object using an image reconstruction algorithm which relies on an accurate model of the electrical interaction. The process of property estimation, called inverse problem, is highly ill-posed and it requires a Regularization method.
The objective of this Thesis was to develop a device for imaging using the EIT technique, which was convenient, noninvasive, easily programmable, portable and relatively cheap in contrast to many other diagnostic tool.
In this direction a simple EIT system and its hardware and software parts are developed.
The data processing was accomplished by utilizing the EIDORS toolkit, which was developed for application to this nonlinear and ill-posed inverse problem.
Experiments have indicated that the EIT system can reconstruct resistive and capacitive images of good contrast despite errors in the measurement are not taken in account
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