A soft-threshold filtering EM-TV algorithm for CT reconstruction

Suyun, Niu; Chen, Ping; Pan, Jiluan; Zhai, Yayu

doi:10.1016/j.ijleo.2014.08.145

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Fortunately, iterative reconstruction algorithm can reconstruct incomplete projection numbers [18]- [19] by incorporating prior knowledge (e.g. non-negative) [20]- [21] and selecting different objective functions according to reconstruction purpose [22]. Though this algorithm has a shortcoming of slow image reconstruction speed, the reconstruction time can be negligible compared to the average FNCT measurement time.…”

Section: Introductionmentioning

confidence: 99%

A MLEM-TV-MRP Algorithm for Fast Neutron Computed Tomography Reconstruction of High Statistical Noise and Sparse Sampling

Dong

Gan

et al. 2020

IEEE Access

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Fast neutron computed tomography (FNCT) generally needs a longer measurement time because of low source strength and low detection efficiency. In order to reduce measurement time, methods of reducing single measurement time and the numbers of projection are employed, but, the two reductions lead to high statistics noise projections and sparse sampling. A good reconstruction algorithm can reduce the influence of these problems for reconstructed images. In this study, the maximum likelihood-expectation maximization algorithm (MLEM) based on the total variation algorithm (TV) and median root prior algorithm (MRP), named MLEM-TV-MRP has been proposed. The proposal incorporates the assumption of the Poisson distribution of projection noises, gradient image sparseness, and locally monotonous. The use of MLEM in the proposal was for reducing the influence of high statistics noise by considering the statistical characteristics of projection noise; the TV algorithm was employed to decrease the influence of sparse sampling by considering gradient image sparseness. Also, the MRP was introduced to remove artifacts and noise by considering the locally monotonous. In addition, a consistency-controlled steepest descent (CCSD) method and TV change method were respectively employed to adaptively adjust the iteration step-size of TV iteration and adaptively stop TV iteration in the proposed algorithm. A classical noise-free phantom was used to initially test the performance of the proposed algorithm− the results showed high-quality reconstruction. To further illustrate its performance in FNCT, a lead-polyethylene (Pb-CH2) sample with 25 high statistics noise projections was employed. SNR value of MLEM-TV-MRP showed an increase of about 62%, 40.7%, 36.7%, and 12.6% respectively as compared to the single-use of MLEM, MLEM-MRP, TV-POCS (projection on convex sets) and MLEM-TV. Also, the profile of the MLEM-TV-MRP algorithm is found to be closest to that of a reference image. In addition, the MLEM-TV-MRP has good convergence performance especlially in convergence value. The results have demonstrated that the proposed algorithm can greatly reduce the influences of few high statistical noise projections on reconstruction in FNCT.INDEX TERMS Fast neutron computed tomography, maximum likelihood-expectation maximization, total variation, median root prior.

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Section: Introductionmentioning

confidence: 99%

A MLEM-TV-MRP Algorithm for Fast Neutron Computed Tomography Reconstruction of High Statistical Noise and Sparse Sampling

Dong

Gan

et al. 2020

IEEE Access

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X-ray energy self-adaption high dynamic range (HDR) imaging based on linear constraints with variable energy

Han

Chen

2017

IEEE Photonics J.

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A Critical Survey on Developed Reconstruction Algorithms for Computed Tomography Imaging from a Limited Number of Projections

Islam

2022

Int. J. Image Grap.

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Rapid system and hardware development of X-ray computed tomography (CT) technologies has been accompanied by equally exciting advances in image reconstruction algorithms. Of the two reconstruction algorithms, analytical and iterative, iterative reconstruction (IR) algorithms have become a clinically viable option in CT imaging. The first CT scanners in the early 1970s used IR algorithms, but lack of computation power prevented their clinical use. In 2009, the first IR algorithms became commercially available and replaced conventionally established analytical algorithms as filtered back projection. Since then, IR has played a vital role in the field of radiology. Although all available IR algorithms share the common mechanism of artifact reduction and/or potential for radiation dose reduction, the magnitude of these effects depends upon specific IR algorithms. IR reconstructs images by iteratively optimizing an objective function. The objective function typically consists of a data integrity term and a regularization term. Therefore, different regularization priors are used in IR algorithms. This paper will briefly look at the overall evolution of CT image reconstruction and the regularization priors used in IR algorithms. Finally, a discussion is presented based on the reality of various reconstruction methodologies at a glance to find the preferred one. Consequently, we will present anticipation towards future advancements in this domain.

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A soft-threshold filtering EM-TV algorithm for CT reconstruction

Cited by 4 publications

References 14 publications

A MLEM-TV-MRP Algorithm for Fast Neutron Computed Tomography Reconstruction of High Statistical Noise and Sparse Sampling

A MLEM-TV-MRP Algorithm for Fast Neutron Computed Tomography Reconstruction of High Statistical Noise and Sparse Sampling

X-ray energy self-adaption high dynamic range (HDR) imaging based on linear constraints with variable energy

A Critical Survey on Developed Reconstruction Algorithms for Computed Tomography Imaging from a Limited Number of Projections

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