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Computationally Efficient Sparse Prior in Regularized Iterative Tomographic Reconstruction

Abstract : X-Ray computed tomography (CT) is a technique that aims at providing a measure of a given property of the interior of a physical object, given a set of exterior projection measurement. Although CT is a mature technology, most of the algorithm used for image reconstruction in commercial applications are based on analytical methods such as the filtered back-projection. The main idea of this thesis is to exploit the latest advances in the field of applied mathematics and computer sciences in order to study, design and implement algorithms dedicated to 3D cone beam reconstruction from X-Ray flat panel detectors targeting clinically relevant usecases, including low doses and few view acquisitions.In this work, we studied various strategies to model the tomographic operators, and how they can be implemented on a multi-GPU platform. Then we proposed to use the 3D complex wavelet transform in order to regularize the reconstruction problem.
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Submitted on : Wednesday, November 29, 2017 - 9:01:33 PM
Last modification on : Tuesday, October 6, 2020 - 12:38:02 PM


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  • HAL Id : tel-01652071, version 1



Thibault Notargiacomo. Computationally Efficient Sparse Prior in Regularized Iterative Tomographic Reconstruction. Automatic. Université Grenoble Alpes, 2017. English. ⟨NNT : 2017GREAT013⟩. ⟨tel-01652071⟩



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