A fast gradient-based algorithm for image reconstruction in inverse geometry CT architecture with sparse distributed sources

Frédéric Jolivet; Clarisse Fournier; Andrea Brambilla

doi:10.1117/12.2534825

28 May 2019 A fast gradient-based algorithm for image reconstruction in inverse geometry CT architecture with sparse distributed sources

Frédéric Jolivet, Clarisse Fournier, Andrea Brambilla

Author Affiliations +

Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 110721N (2019) https://doi.org/10.1117/12.2534825
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States

Abstract

Conventional Cone Beam CT (CBCT) is composed of a single source and a large detector to aquire a full sinogram of the object. Multi-source inverse geometry CT system (IGCT) consists, for its part in using several sources and a small detector to acquire several partial sinograms of the object. For technological, financial and medical reasons the reduction of the number of sources and the reduction of the detector size are interesting but induce to solve an ill-posed and ill-conditionned problem. We propose a regularized iterative algorithm which is able to reconstruct the object volume from partial sinograms acquired with a an optimized multi-source IGCT system : we will demonstrate the performance of the proposed algorithm when we reduce the size of the detector and the number of sources. Realistically simulated CT data is reconstructed with the proposed algorithm and the results are compared to those obtained by filtered backprojection (FBP) and those obtained by a maximum likelihood estimation to show the impact of the regularization.

Citation Download Citation

Frédéric Jolivet, Clarisse Fournier, and Andrea Brambilla "A fast gradient-based algorithm for image reconstruction in inverse geometry CT architecture with sparse distributed sources", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110721N (28 May 2019); https://doi.org/10.1117/12.2534825

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