Adaptive iterative reconstruction based on relative total variation for low-intensity computed tomography

Gong, Changcheng; Zeng, Li

doi:10.1016/j.sigpro.2019.06.031

Cited by 40 publications

(20 citation statements)

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“…In our following work, we will explore the automatic strategies to select different parameters [46], [47]. The L-curve-based adaptive parameter selection methods have demonstrated the great potential of determining regularized parameters for CT imaging [48]. It is feasible to optimize L-curve in our future work.…”

Section: Discussionmentioning

confidence: 99%

Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19

et al. 2021

IEEE Trans. Instrum. Meas.

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Methods to recover high-quality computed tomography (CT) images in low-dose cases will be of great benefit. To reach this goal, sparse-data subsampling is one of the common strategies to reduce radiation dose, which is attracting interest among the researchers in the CT community. Since analytic image reconstruction algorithms may lead to severe image artifacts, the iterative algorithms have been developed for reconstructing images from sparsely sampled projection data. In this study, we first develop a tensor gradient L 0 -norm minimization (TGLM) for low-dose CT imaging. Then, the TGLM model is optimized by using the split-Bregman method. The Coronavirus Disease 2019 (COVID-19) has been sweeping the globe, and CT imaging has been deployed for detection and assessing the severity of the disease. Finally, we first apply our proposed TGLM method for COVID-19 to achieve low-dose scan by incorporating the 3-D spatial information. Two COVID-19 patients (64 years old female and 56 years old man) were scanned by the µCT 528 system, and the acquired projections were retrieved to validate and evaluate the performance of the TGLM.

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Section: Discussionmentioning

confidence: 99%

Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19

et al. 2021

IEEE Trans. Instrum. Meas.

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“…where D x or D y and L x or L y are general pixel-wise weights, which are controlled by multiplying a weighting function of the Gaussian distribution ρ. h is the spatial scale parameter of the window for determining the remaining detail and salient structure, and ε, which represents any small positive number close to zero, is used to prevent numerical instability. The RTV model does not require a prior type of texture; instead, it produces a novel map depending on the inherent windowed variation [34][35][36]. Its map has an effect on adaptive edge preservation for veins and removing the other texture in parallel.…”

Section: Proposed Vein Recognition Techniquementioning

confidence: 99%

Performance Evaluation of Dorsal Vein Network of Hand Imaging Using Relative Total Variation-Based Regularization for Smoothing Technique in a Miniaturized Vein Imaging System: A Pilot Study

Kim

Jeong

Lee

2021

IJERPH

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Vein puncture is commonly used for blood sampling, and accurately locating the blood vessel is an important challenge in the field of diagnostic tests. Imaging systems based on near-infrared (NIR) light are widely used for accurate human vein puncture. In particular, segmentation of a region of interest using the obtained NIR image is an important field, and research for improving the image quality by removing noise and enhancing the image contrast is being widely conducted. In this paper, we propose an effective model in which the relative total variation (RTV) regularization algorithm and contrast-limited adaptive histogram equalization (CLAHE) are combined, whereby some major edge information can be better preserved. In our previous study, we developed a miniaturized NIR imaging system using light with a wavelength of 720–1100 nm. We evaluated the usefulness of the proposed algorithm by applying it to images acquired by the developed NIR imaging system. Compared with the conventional algorithm, when the proposed method was applied to the NIR image, the visual evaluation performance and quantitative evaluation performance were enhanced. In particular, when the proposed algorithm was applied, the coefficient of variation was improved by a factor of 15.77 compared with the basic image. The main advantages of our algorithm are the high noise reduction efficiency, which is beneficial for reducing the amount of undesirable information, and better contrast. In conclusion, the applicability and usefulness of the algorithm combining the RTV approach and CLAHE for NIR images were demonstrated, and the proposed model can achieve a high image quality.

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“…Therefore, the fidelity of image gradients in x direction becomes stronger under the constraints of projections, and ARTV model can enhance weak structures in limited-angle CT reconstruction. A two-step algorithm has been used to solve a CT reconstruction model based on RTV iteratively [27]. We also use this algorithm to solve problem (11).…”

Section: ) Anisotropic Relative Total Variationmentioning

confidence: 99%

“…The numerical solution of this problem can be solved iteratively. Following the strategy in reference [27], we rewritten x part of the measure of ARTV as follows,…”

Section: ) Anisotropic Relative Total Variationmentioning

confidence: 99%

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Self-Guided Limited-Angle Computed Tomography Reconstruction Based on Anisotropic Relative Total Variation

Gong

Zeng

2020

IEEE Access

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In some computed tomography (CT) applications, limited-angle projections are used for image reconstruction, and traditional reconstruction methods, such as filtered back-projection (FBP) or simultaneous algebraic reconstruction technique (SART), cannot reconstruct high-quality CT images without prior knowledge assistance. For limited-angle CT reconstruction, total variation minimization (TVM) method is not conductive to recovering image structures. Image reconstruction methods based on anisotropic total variation (ATV) and reweighted anisotropic total variation (RwATV) can significantly reduce the shading artifacts using prior knowledge of the scanning angular range and image sparsity. However, using the knowledge of image sparsity does not further improve image quality in some applications. In this paper, we propose a new reconstruction method based on anisotropic relative total variation (ARTV) for limited-angle CT reconstruction. In ARTV, the windowed inherent variation (WIV) indicates the strength of structure information and WIV values are adaptively determined by local structure information. In limitedangle CT, particular scanning angular range urges us into exerting different strengths on different directions of ARTV. Experiments on FORBILD HEAD phantom, a thoracic image and real projections of a walnut are performed to test the new method. Experimental results show that the destroyed structures are recovered to some extent, and we acquire higher image quality compared to some existing limited-angle CT reconstruction methods. INDEX TERMS Computed tomography, image reconstruction, limited-angle computed tomography, anisotropic total variation, relative total variation.

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Adaptive iterative reconstruction based on relative total variation for low-intensity computed tomography

Cited by 40 publications

References 50 publications

Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19

Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19

Performance Evaluation of Dorsal Vein Network of Hand Imaging Using Relative Total Variation-Based Regularization for Smoothing Technique in a Miniaturized Vein Imaging System: A Pilot Study

Self-Guided Limited-Angle Computed Tomography Reconstruction Based on Anisotropic Relative Total Variation

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