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Reconstruction itérative en tomographie à rayons X pour une géométrie inverse avec sources distribuées

Abstract : A conventional Cone Beam Computed Tomography (CBCT) architecture is composed of a single source and a large detector to aquire a full sinogram of the object. In opposite a Multi-Source Inverse Geometry Computed Tomography (MS-IGCT) architecture propose to use several sources and a small detector to acquire several truncated sinograms of the object. Using few sources while keeping a small detector size is a key issue for technological, financial and in some cases, dose reasons. However in this configuration the object reconstruction induces to solve an ill-posed and ill-conditionned problem. We propose a regularized iterative algorithm which is able to reconstruct the object volume from sinograms acquired with an optimized MS-IGCT : we will demonstrate the performance of the proposed algorithm when we reduce the size of the detector and the number of sources. In a second step we show that taking the inverse of the noise covariance matrix into account reduces dramatically metals artifacts due to the high density of the reconstructed object. Realistically simulated CT data is reconstructed with the proposed algorithm and the results are compared to those obtained by filtered backprojection (FBP).
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Contributor : Frédéric Jolivet <>
Submitted on : Wednesday, September 18, 2019 - 9:53:40 AM
Last modification on : Thursday, June 11, 2020 - 5:04:09 PM
Long-term archiving on: : Saturday, February 8, 2020 - 10:09:55 PM

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  • HAL Id : cea-02290895, version 1

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Frédéric Jolivet, Clarisse Fournier, Joachim Tabary, Lenka Zdeborová, Andrea Brambilla. Reconstruction itérative en tomographie à rayons X pour une géométrie inverse avec sources distribuées. XXVIIème coloque GRETSI, Aug 2019, Lille, France. ⟨cea-02290895⟩

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