Median prior constrained TV algorithm for sparse view low-dose CT reconstruction

Liu, Yi; Shangguan, Hong; Zhang, Quan; Zhu, Hongqing; Hu, Shu; Gui, Zhiguo

doi:10.1016/j.compbiomed.2015.03.003

Cited by 29 publications

(13 citation statements)

References 26 publications

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“…A high‐accuracy system matrix G is important for CT image reconstruction. G could be determined by many methods (Turkington, ; Liu et al, ), e.g., the view‐of‐view method presented by Vardi et al (), and the Monte Carlo (MC) method presented by Veklerov et al (). Since the research focus of this article is not the system matrix G , we choose the linear distance method to compute the system matrix (Liu et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…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%

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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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Section: Methodsmentioning

confidence: 99%

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

Self Cite

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“…The Proposed Algorithm 1: Initialize parameters, tol = 10 −6 , A as the known CT system matrix, ψ as the known sparse representation matrix, 2: Set h 0 is a Dirac delta function 3: Input the known noisy projection data P 4: for each iteration do 5: while h(l + 1) − h(l) / h(l) > tol do 6: Calculatingx according to (9) 7: CalculatingV according to (14) 8: Calculatingf according tof = g(x) V 9: Sampling h according to (18) and (19) with a Gibbs method [24] 10: end while 11: end for 12: return To reconstruct CT image with far fewer projection data, a compressed sensing image reconstruction algorithm including a blind image restoration process was proposed. Image reconstruction is achieved by estimating a sparse coefficient vector.…”

Section: Of 15mentioning

confidence: 99%

A Novel Blind Restoration and Reconstruction Approach for CT Images Based on Sparse Representation and Hierarchical Bayesian-MAP

Sun

Zhang

et al. 2019

Algorithms

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Computed tomography (CT) image reconstruction and restoration are very important in medical image processing, and are associated together to be an inverse problem. Image iterative reconstruction is a key tool to increase the applicability of CT imaging and reduce radiation dose. Nevertheless, traditional image iterative reconstruction methods are limited by the sampling theorem and also the blurring of projection data will propagate unhampered artifact in the reconstructed image. To overcome these problems, image restoration techniques should be developed to accurately correct a wide variety of image degrading effects in order to effectively improve image reconstruction. In this paper, a blind image restoration technique is embedded in the compressive sensing CT image reconstruction, which can result in a high-quality reconstruction image using fewer projection data. Because a small amount of data can be obtained by radiation in a shorter time, high-quality image reconstruction with less data is equivalent to reducing radiation dose. Technically, both the blurring process and the sparse representation of the sharp CT image are first modeled as a serial of parameters. The sharp CT image will be obtained from the estimated sparse representation. Then, the model parameters are estimated by a hierarchical Bayesian maximum posteriori formulation. Finally, the estimated model parameters are optimized to obtain the final image reconstruction. We demonstrate the effectiveness of the proposed method with the simulation experiments in terms of the peak signal to noise ratio (PSNR), and structural similarity index (SSIM).

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“…The simulation was performed at 80 kV, and the corresponding spectrum ( Fig. 3) was divided into six energy bins: [20,30] keV, [30,40] keV, [40,50] keV, [50,60]…”

Section: Digital Circle Phantom-mentioning

confidence: 99%

“…Compressed sensing has recently been evaluated by several groups for spectral CT reconstruction from noisy and incomplete measurement [15,18,28,[35][36][37][38][39][40]. The key for the success of CS is the use of the sparse transform guided by prior information.…”

Section: Introductionmentioning

confidence: 99%

Iterative reconstruction for photon-counting CT using prior image constrained total generalized variation

Niu

Zhang

Zhong

et al. 2018

Computers in Biology and Medicine

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Shanzhou Niu received his Ph.D. degree in biomedical engineering from Southern Medical University in 2015. Then he joined the School of Mathematics and Computer Science at the Gannan Normal University. He is currently a postdoctoral researcher in the Department of Radiation Oncology at UT Southwestern Medical Center. His research interests include CT imaging, image processing, and optimization theory and methods. You Zhang received his Ph.D. degree in biomedical engineering from Duke University in 2015. He is currently an assistant professor in the Department of Radiation Oncology at the UT Southwestern Medical Center. His research interests include CT/CBCT reconstruction and image registration. Yuncheng Zhong is currently an instructor in the Department of Radiation Oncology at the UT Southwestern Medical Center. His research interests include CT and PET imaging. Guoliang liu is currently an associated professor in the School of Information Engineering at the Gannan Medical Unviersity. His research interest is CT imaging. Shaohui Lu is currently an associated professor in the First Affiliated Hospital of Gannan Medical University. His research interesting is perfusion CT imaging.

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Median prior constrained TV algorithm for sparse view low-dose CT reconstruction

Cited by 29 publications

References 26 publications

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

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

A Novel Blind Restoration and Reconstruction Approach for CT Images Based on Sparse Representation and Hierarchical Bayesian-MAP

Iterative reconstruction for photon-counting CT using prior image constrained total generalized variation

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