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Region-of-Interest (ROI) tomography aims at reconstructing a region of interest \begin{document} $C$ \end{document} inside a body using only x-ray projections intersecting \begin{document} $C$ \end{document} and it is useful to reduce overall radiation exposure when only a small specific region of a body needs to be examined. We consider x-ray acquisition from sources located on a smooth curve \begin{document} $Γ$ \end{document} in \begin{document} $\mathbb R^3$ \end{document} verifying the classical Tuy condition. static electricity zapper In this generic situation, the non- trucated cone-beam transform of smooth density functions \begin{document} $f$ \end{document} admits an explicit inverse \begin{document} $Z$ \end{document} as originally shown by Grangeat. However \begin{document} $Z$ \end{document} cannot directly reconstruct \begin{document} $f$ \end{document} from ROI-truncated projections. To deal with the ROI tomography problem, we introduce a novel reconstruction approach. year 6 electricity For densities \begin{document} $f$ \end{document} in \begin{document} $L^{∞}(B)$ \end{document} where \begin{document} $B$ \end{document} is a bounded ball in \begin{document} $\mathbb R^3$ \end{document}, our method iterates an operator \begin{document} $U$ \end{document} combining ROI-truncated projections, inversion by the operator \begin{document} $Z$ \end{document} and appropriate regularization operators. Assuming only knowledge of projections corresponding to a spherical ROI \begin{document} $C \subset B$ \end{document}, given \begin{document} $ε >0$ \end{document}, we prove that if \begin{document} $C$ \end{document} is sufficiently large our iterative reconstruction algorithm converges at exponential speed to an \begin{document} $ε$ \end{document}-accurate approximation of \begin{document} $f$ \end{document} in \begin{document} $L^{∞}$ \end{document}. The accuracy depends on the regularity of \begin{document} $f$ \end{document} quantified by its Sobolev norm in \begin{document} $W^5(B)$ \end{document}. Our result guarantees the existence of a critical ROI radius ensuring the convergence of our ROI reconstruction algorithm to an \begin{document} $ε$ \end{document}-accurate approximation of \begin{document} $f$ \end{document}. grade 9 electricity module We have numerically verified these theoretical results using simulated acquisition of ROI-truncated cone-beam projection data for multiple acquisition geometries. Numerical experiments indicate that the critical ROI radius is fairly small with respect to the support region \begin{document} $B$ \end{document}.

Inverse transport theory concerns the reconstruction of the absorption and scattering coefficients in a transport equation from knowledge of the albedo operator, which models all possible boundary measurements. Uniqueness and stability results are well known and are typically obtained for errors of the albedo operator measured in the \begin{document} $L^1$ \end{document} sense. e suvidha electricity bill lucknow We claim that such error estimates are not always very informative. For instance, arbitrarily small blurring and misalignment of detectors result in \begin{document} $O(1)$ \end{document} errors of the albedo operator and hence in \begin{document} $O(1)$ \end{document} error predictions on the reconstruction of the coefficients, which are not useful.

This paper revisit such stability estimates by introducing a more forgiving metric on the measurements errors, namely the \begin{document} $1-$ \end{document}Wasserstein distances, which penalize blurring or misalignment by an amount proportional to the width of the blurring kernel or to the amount of misalignment. We obtain new stability estimates in this setting.

We also consider the effect of errors, still measured in the \begin{document} $1-$ \end{document} Wasserstein distance, on the generation of the probing source. This models blurring and misalignment in the design of (laser) probes and allows us to consider discretized sources. electricity around the world Under appropriate assumptions on the coefficients, we quantify the effect of such errors on the reconstructions.

We investigate in this paper the diffusion magnetic resonance imaging (MRI) in deformable organs such as the living heart. gas mask bong how to use The difficulty comes from the hight sensitivity of diffusion measurement to tissue motion. Commonly in literature, the diffusion MRI signal is given by the complex magnetization of water molecules described by the Bloch-Torrey equation. When dealing with deformable organs, the Bloch-Torrey equation is no longer valid. electricity cost calculator Our main contribution is then to introduce a new mathematical description of the Bloch-Torrey equation in deforming media. In particular, some numerical simulations are presented to quantify the influence of cardiac motion on the estimation of diffusion. Moreover, based on a scaling argument and on an asymptotic model for the complex magnetization, we derive a new apparent diffusion coefficient formula. j gastroenterol hepatol Finally, some numerical experiments illustrate the potential of this new version which gives a better reconstruction of the diffusion than using the classical one.

Digital tomographic image reconstruction uses multiple x-ray projections obtained along a range of different incident angles to reconstruct a 3D representation of an object. For example, computed tomography (CT) generally refers to the situation when a full set of angles are used (e.g., 360 degrees) while tomosynthesis refers to the case when only a limited (e.g., 30 degrees) angular range is used. In either case, most existing reconstruction algorithms assume that the x-ray source is monoenergetic. This results in a simplified linear forward model, which is easy to solve but can result in artifacts in the reconstructed images. It has been shown that these artifacts can be reduced by using a more accurate polyenergetic assumption for the x-ray source, but the polyenergetic model requires solving a large-scale nonlinear inverse problem. In addition to reducing artifacts, a full polyenergetic model can be used to extract additional information about the materials of the object; that is, to provide a mechanism for quantitative imaging. In this paper, we develop an approach to solve the nonlinear image reconstruction problem by incorporating total variation (TV) regularization. The corresponding optimization problem is then solved by using a scaled gradient descent method. The proposed algorithm is based on KKT conditions and Nesterov’s acceleration strategy. Experimental results on reconstructed polyenergetic image data illustrate the effectiveness of this proposed approach.