Literature DB >> 19673214

An analytical approach to estimating the first order scatter in heterogeneous medium. II. A practical application.

Weiguang Yao1, Konrad W Leszczynski.   

Abstract

Recently, the authors proposed an analytical scheme to estimate the first order x-ray scatter by approximating the Klein-Nishina formula so that the first order scatter fluence is expressed as a function of the primary photon fluence on the detector. In this work, the authors apply the scheme to experimentally obtained 6 MV cone beam CT projections in which the primary photon fluence is the unknown of interest. With the assumption that the higher-order scatter fluence is either constant or proportional to the first order scatter fluence, an iterative approach is proposed to estimate both primary and scatter fluences from projections by utilizing their relationship. The iterative approach is evaluated by comparisons with experimentally measured scatter-primary ratios of a Catphan phantom and with Monte Carlo simulations of virtual phantoms. The convergence of the iterations is fast and the accuracy of scatter correction is high. For a sufficiently long cylindrical water phantom with 10 cm of radius, the relative error of estimated primary photon fluence was within +/- 2% and +/- 4% when the phantom was projected with 6 MV and 120 kVp x-ray imaging systems, respectively. In addition, the iterative approach for scatter estimation is applied to 6 MV x-ray projections of a QUASAR and anthropomorphic phantoms (head and pelvis). The scatter correction is demonstrated to significantly improve the accuracy of the reconstructed linear attenuation coefficient and the contrast of the projections and reconstructed volumetric images generated with a linac 6 MV beam.

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Year:  2009        PMID: 19673214     DOI: 10.1118/1.3152115

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  3 in total

Review 1.  Modelling the physics in the iterative reconstruction for transmission computed tomography.

Authors:  Johan Nuyts; Bruno De Man; Jeffrey A Fessler; Wojciech Zbijewski; Freek J Beekman
Journal:  Phys Med Biol       Date:  2013-06-05       Impact factor: 3.609

2.  Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network.

Authors:  Satoshi Kida; Takahiro Nakamoto; Masahiro Nakano; Kanabu Nawa; Akihiro Haga; Jun'ichi Kotoku; Hideomi Yamashita; Keiichi Nakagawa
Journal:  Cureus       Date:  2018-04-29

3.  Feasibility of improving cone-beam CT number consistency using a scatter correction algorithm.

Authors:  Jun Li; Weiguang Yao; Ying Xiao; Yan Yu
Journal:  J Appl Clin Med Phys       Date:  2013-11-04       Impact factor: 2.102

  3 in total

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