Correction to “Total Variation-Stokes Strategy for Sparse-View X-ray CT Image Reconstruction” [Mar 14 749-763]

BackgroundThe sparse CT (Computed Tomography), inspired by compressed sensing, means to introduce a prior information of image sparsity into CT reconstruction to reduce the input projections so as to reduce the potential threat of incremental X-ray dose to patients’ health. Recently, many remarkable works were concentrated on the sparse CT reconstruction from sparse (limited-angle or few-view style) projections. In this paper we would like to incorporate more prior information into the sparse CT reconstruction for improvement of performance. It is known decades ago that the given projection directions can provide information about the directions of edges in the restored CT image. ATV (Anisotropic Total Variation), a TV (Total Variation) norm based regularization, could use the prior information of image sparsity and edge direction simultaneously. But ATV can only represent the edge information in few directions and lose much prior information of image edges in other directions.MethodsTo sufficiently use the prior information of edge directions, a novel MDATV (Multi-Direction Anisotropic Total Variation) is proposed. In this paper we introduce the 2D-IGS (Two Dimensional Image Gradient Space), and combined the coordinate rotation transform with 2D-IGS to represent edge information in multiple directions. Then by incorporating this multi-direction representation into ATV norm we get the MDATV regularization. To solve the optimization problem based on the MDATV regularization, a novel ART (algebraic reconstruction technique) + MDATV scheme is outlined. And NESTA (NESTerov’s Algorithm) is proposed to replace GD (Gradient Descent) for minimizing the TV-based regularization.ResultsThe numerical and real data experiments demonstrate that MDATV based iterative reconstruction improved the quality of restored image. NESTA is more suitable than GD for minimization of TV-based regularization.ConclusionsMDATV regularization can sufficiently use the prior information of image sparsity and edge information simultaneously. By incorporating more prior information, MDATV based approach could reconstruct the image more exactly.

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“…UQI measures the similarity between the desired image and its ground truth image [23]. UQI value ranges between zero and one.…”

Section: Simulations and Experimentsmentioning

confidence: 99%

Sparse CT reconstruction based on multi-direction anisotropic total variation (MDATV)

Chen

Wang

et al. 2014

BioMed Eng OnLine

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“…One type of commonly used regularization is the MRF‐based penalty (Zhang et al, ), which penalizes the differences among local neighboring pixels but tends to achieve undesirable oversmoothing at the edge regions. In order to address this drawback, several edge‐preserving regularizations (Liu et al, ; Liu et al, ) were utilized later, such as the Huber prior, in which the penalty function is nonquadratic, and the median prior, which only relies on the properties of the local pixel region (Kontaxakis et al, ; Yan and Yu, ).…”

Section: Introductionmentioning

confidence: 99%

Sparse‐view statistical iterative head CT image reconstruction via joint regularization

Shangguan

Liu

Cui

et al. 2016

Int J Imaging Syst Tech

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It is a significant challenge to accurately reconstruct medical computed tomography (CT) images with important details and features. Reconstructed images always suffer from noise and artifact pollution because the acquired projection data may be insufficient or undersampled. In reality, some “isolated noise points” (similar to impulse noise) always exist in low‐dose CT projection measurements. Statistical iterative reconstruction (SIR) methods have shown greater potential to significantly reduce quantum noise but still maintain the image quality of reconstructions than the conventional filtered back‐projection (FBP) reconstruction algorithm. Although the typical total variation‐based SIR algorithms can obtain reconstructed images of relatively good quality, noticeable patchy artifacts are still unavoidable. To address such problems as impulse‐noise pollution and patchy‐artifact pollution, this work, for the first time, proposes a joint regularization constrained SIR algorithm for sparse‐view CT image reconstruction, named “SIR‐JR” for simplicity. The new joint regularization consists of two components: total generalized variation, which could process images with many directional features and yield high‐order smoothness, and the neighborhood median prior, which is a powerful filtering tool for impulse noise. Subsequently, a new alternating iterative algorithm is utilized to solve the objective function. Experiments on different head phantoms show that the obtained reconstruction images are of superior quality and that the presented method is feasible and effective.

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“…The TV minimization methods are fairly successful and can produce almost artifact-free images in few-view low-noise tomography [5]–[12]. As an alternative methed, a model-based solution is suggested in [13]. …”

Section: Introductionmentioning

confidence: 99%

On Few-View Tomography and Staircase Artifacts

Zeng

2015

IEEE Trans. Nucl. Sci.

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This paper investigates some potential methods for few-view tomography, and investigates the cause and remedy of the staircase artifacts. This paper also discusses whether there is any benefit to use edge-preserving filters in emission tomography. We formulate a general Green’s one-step-late algorithm, so that it can incorporate any linear or non-linear filters. We argue that the derivative of the penalty function can be “artificially” created, not naturally derived from a penalty function. We have gained more insight into constrained image reconstruction especially with edge-preserving constraints. Our numerical results show that the bilateral method can have better performance than the TV method, and higher order methods may not be necessary for non-piecewise-constant objects.

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Correction to “Total Variation-Stokes Strategy for Sparse-View X-ray CT Image Reconstruction” [Mar 14 749-763]

Cited by 48 publications

References 1 publication

Sparse CT reconstruction based on multi-direction anisotropic total variation (MDATV)

Sparse CT reconstruction based on multi-direction anisotropic total variation (MDATV)

Sparse‐view statistical iterative head CT image reconstruction via joint regularization

On Few-View Tomography and Staircase Artifacts

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