Literature DB >> 24191986

Directional sinogram interpolation for sparse angular acquisition in cone-beam computed tomography.

Hua Zhang1, Jan-Jakob Sonke.   

Abstract

Cone-beam (CB) computed tomography (CT) is widely used in the field of medical imaging for guidance. Inspired by Betram's directional interpolation (BDI) methods, directional sinogram interpolation (DSI) was implemented to generate more CB projections by optimized (iterative) double-orientation estimation in sinogram space and directional interpolation. A new CBCT was subsequently reconstructed with the Feldkamp algorithm using both the original and interpolated CB projections. The proposed method was evaluated on both phantom and clinical data, and image quality was assessed by correlation ratio (CR) between the interpolated image and a gold standard obtained from full measured projections. Additionally, streak artifact reduction and image blur were assessed. In a CBCT reconstructed by 40 acquired projections over an arc of 360 degree, streak artifacts dropped 20.7% and 6.7% in a thorax phantom, when our method was compared to linear interpolation (LI) and BDI methods. Meanwhile, image blur was assessed by a head-and-neck phantom, where image blur of DSI was 20.1% and 24.3% less than LI and BDI. When our method was compared to LI and DI methods, CR increased by 4.4% and 3.1%. Streak artifacts of sparsely acquired CBCT were decreased by our method and image blur induced by interpolation was constrained to below other interpolation methods.

Entities:  

Keywords:  Cone-beam CT; directional interpolation; sinogram interpolation; sparse acquisition; streak artifacts

Mesh:

Year:  2013        PMID: 24191986     DOI: 10.3233/XST-130401

Source DB:  PubMed          Journal:  J Xray Sci Technol        ISSN: 0895-3996            Impact factor:   1.535


  5 in total

1.  Hepatic dual-contrast CT imaging: slow triple kVp switching CT with CNN-based sinogram completion and material decomposition.

Authors:  Wenchao Cao; Nadav Shapira; Andrew Maidment; Heiner Daerr; Peter B Noël
Journal:  J Med Imaging (Bellingham)       Date:  2022-01-31

2.  Temporally downsampled cerebral CT perfusion image restoration using deep residual learning.

Authors:  Haichen Zhu; Dan Tong; Lu Zhang; Shijie Wang; Weiwen Wu; Hui Tang; Yang Chen; Limin Luo; Jian Zhu; Baosheng Li
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-10-31       Impact factor: 2.924

3.  A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram.

Authors:  Dimitrios Bellos; Mark Basham; Tony Pridmore; Andrew P French
Journal:  J Synchrotron Radiat       Date:  2019-04-23       Impact factor: 2.616

4.  Cubic-Spline Interpolation for Sparse-View CT Image Reconstruction With Filtered Backprojection in Dynamic Myocardial Perfusion Imaging.

Authors:  Esmaeil Enjilela; Ting-Yim Lee; Gerald Wisenberg; Patrick Teefy; Rodrigo Bagur; Ali Islam; Jiang Hsieh; Aaron So
Journal:  Tomography       Date:  2019-09

5.  Sparse-view tomography via displacement function interpolation.

Authors:  Gengsheng L Zeng
Journal:  Vis Comput Ind Biomed Art       Date:  2019-11-12
  5 in total

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