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      Linearized internal functionals for anisotropic conductivities

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          Abstract

          This paper concerns the reconstruction of an anisotropic conductivity tensor in an elliptic second-order equation from knowledge of the so-called power density functionals. This problem finds applications in several coupled-physics medical imaging modalities such as ultrasound modulated electrical impedance tomography and impedance-acoustic tomography. We consider the linearization of the nonlinear hybrid inverse problem. We find sufficient conditions for the linearized problem, a system of partial differential equations, to be elliptic and for the system to be injective. Such conditions are found to hold for a lesser number of measurements than those required in recently established explicit reconstruction procedures for the nonlinear problem.

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          Electrical Impedance Tomography by Elastic Deformation

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            Imaging by Modification: Numerical Reconstruction of Local Conductivities from Corresponding Power Density Measurements

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              2D and 3D reconstructions in acousto-electric tomography

              We propose and test stable algorithms for the reconstruction of the internal conductivity of a biological object using acousto-electric measurements. Namely, the conventional impedance tomography scheme is supplemented by scanning the object with acoustic waves that slightly perturb the conductivity and cause the change in the electric potential measured on the boundary of the object. These perturbations of the potential are then used as the data for the reconstruction of the conductivity. The present method does not rely on "perfectly focused" acoustic beams. Instead, more realistic propagating spherical fronts are utilized, and then the measurements that would correspond to perfect focusing are synthesized. In other words, we use \emph{synthetic focusing}. Numerical experiments with simulated data show that our techniques produce high quality images, both in 2D and 3D, and that they remain accurate in the presence of high-level noise in the data. Local uniqueness and stability for the problem also hold.
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                Author and article information

                Journal
                2013-02-14
                Article
                1302.3354
                9e89865e-6fc4-4b46-9d03-13fab5b73d47

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                22 pages, submitted to Inverse Problems and Imaging
                math.AP

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