9 July 2018 Hybrid algorithm for few-views computed tomography of strongly absorbing media: algebraic reconstruction, TV-regularization, and adaptive segmentation
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Abstract
The paper presents an original hybrid image reconstruction algorithm ART-TVS for few-views computed tomography of strongly absorbing media. It is based on the well-known algebraic reconstruction technique (ART), regularization of interim results through minimization of the total variation norm (TV-regularization), and a method of adaptive segmentation, which is a modernization of the known region growing algorithm. It is shown that the ART-TVS algorithm does not give stripe artifacts even if the number of views is very small (eight or less). ART-TVS reconstruction results for two numerical models of metal shells are compared with those obtained with the ART-TV algorithm (ART with TV-regularization and without adaptive segmentation), the iterative Potts minimization algorithm (IPMA), and our MART-AP algorithm (multiplicative ART with a priori information) we developed earlier for few-views discrete tomography. It is shown that ART-TVS outperforms ART-TV and IPMA and is comparable with MART-AP in reconstruction accuracy. Also, ART-TVS converges markedly faster than IRMA in cases where strongly underdetermined systems are treated. The algorithm we propose also demonstrates quite satisfactory resistance to projection data noise that is inherent in tomography of strongly absorbing media.
© 2018 SPIE and IS&T
Vitaly V. Vlasov, Alexander B. Konovalov, Sergey V. Kolchugin, "Hybrid algorithm for few-views computed tomography of strongly absorbing media: algebraic reconstruction, TV-regularization, and adaptive segmentation," Journal of Electronic Imaging 27(4), 043006 (9 July 2018). https://doi.org/10.1117/1.JEI.27.4.043006 . Submission: Received: 18 October 2017; Accepted: 25 May 2018
Received: 18 October 2017; Accepted: 25 May 2018; Published: 9 July 2018
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