Nonlocal Total Variation Using the First and Second Order Derivatives and Its Application to CT image Reconstruction

Kim, Yongchae; Kudo, Hiroyuki

doi:10.3390/s20123494

“…The filtered back projection (FBP) reconstruction algorithm [ 18 ], total variation minimization (TV algorithm) [ 19 ], and block-matched three-dimensional filtering (BM3D algorithm) [ 20 ] were introduced. The normalized mean absolute distance (NMAD), root mean square error (RMSE), structural similarity index measure (SSIM), and peak signal to noise ratio (PSNR) were compared to evaluate the noise reduction effect of the model.…”

Section: Methodsmentioning

confidence: 99%

Effect Evaluation of Platelet-Rich Plasma Combined with Vacuum Sealing Drainage on Serum Inflammatory Factors in Patients with Pressure Ulcer by Intelligent Algorithm-Based CT Image

Yang

¹

,

Xiao

²

,

Wen

³

et al. 2022

Computational and Mathematical Methods in Medicine

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This work was to explore the efficacy of intelligent algorithm-based computed tomography (CT) to evaluate platelet-rich plasma (PRP) combined with vacuum sealing drainage (VSD) in the treatment of patients with pressure ulcers. Based on the u-net network structure, an image denoising algorithm based on double residual convolution neural network (Dr-CNN) was proposed to denoise the CT images. A total of 84 patients who were hospitalized in hospital were randomly divided into group A (without any intervention), group B (PRP treatment), group C (VSD treatment), and group D (PRP+VSD treatment). Procalcitonin (PCT) was detected by enzyme-linked immunosorbent assay (ELISA) combined with immunofluorescence method, C-reactive protein (CRP) was detected by rate reflectance turbidimetry (RRT), and interleukin-6 (IL-6) was detected by electrochemiluminescence method. The results showed that after treatment, 44 cases (52.38%) of pressure ulcers patients recovered, 24 cases (28.57%) had no change in stage, and 16 cases (19.04%) developed pressure ulcers. The pain scores of group D at 1 week ( 3.35 ± 0.56 points) and 2 weeks ( 2.76 ± 0.55 points) after treatment were significantly lower than those in group C ( 7.77 ± 0.58 points and 6.34 ± 0.44 points, respectively). The time of complete wound healing in group D ( 24.5 ± 2.32 ) was obviously lower in contrast to that in groups A, B, and C ( 35.54 ± 3.22 days, 30.23 ± 2 days, and 29.34 ± 2.15 days, respectively). In addition, the medical satisfaction of group D ( 8.74 ± 0.69 ) was significantly higher than that of groups A, B, and C ( 4.69 ± 0.85 , 5.22 ± 0.31 , and 5.18 ± 0.59 , respectively). The levels of IL-6 and PCT in group D were lower than those in groups A, B, and C, and the differences were statistically significant ( P < 0.01 ). The average values of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) of the Dr-CNN network model were 37.21 ± 1.09 dB and 0.925 ± 0.01 , respectively, which were higher than other algorithms. The mean values of root mean square error (MSE) and normalized mean absolute distance (NMAD) of the Dr-CNN network model were 0.022 ± 0.002 and 0.126 ± 0.012 , respectively, which were significantly lower than other algorithms ( P < 0.05 ). The experimental results showed that PrP combined with VSD could significantly reduce the inflammatory response of patients with pressure ulcers. PRP combined with VSD could significantly reduce the pain of dressing change for patients. Moreover, the performance model of image denoising algorithm based on double residual convolutional neural network was better than other algorithms.

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“…A weighted total variation denoising process is applied on the reconstructed images to enhance the intensity difference between the striking features and unsolicited noise by determining the similarities between non-local patches ( 10 , 20 , 37 , 38 ). When ∅ i,j is the area affected by the center point of each patch, the state of the stationary patch is determined by calculating the statistical measurements with the moving patches included in the search set.…”

Section: Methodsmentioning

confidence: 99%

Mutual Information-Based Non-Local Total Variation Denoiser for Low-Dose Cone-Beam Computed Tomography

Lee

¹

,

Sung

²

,

Choi

³

et al. 2021

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Conventional non-local total variation (NLTV) approaches use the weight of a non-local means (NLM) filter, which degrades performance in low-dose cone-beam computed tomography (CBCT) images generated with a low milliampere-seconds (mAs) parameter value because a local patch used to determine the pixel weights comprises noisy-damaged pixels that reduce the similarity between corresponding patches. In this paper, we propose a novel type of NLTV based on a combination of mutual information (MI): MI-NLTV. It is based on a statistical measure for a similarity calculation between the corresponding bins of non-local patches vs. a reference patch. The weight is determined in terms of a statistical measure comprising the MI value between corresponding non-local patches and the reference-patch entropy. The MI-NLTV denoising process is applied to CBCT images generated by the analytical reconstruction algorithm using a ray-driven backprojector (RDB). The MI-NLTV objective function is minimized based on the steepest gradient descent optimization to augment the difference between a real structure and noise, cleaning noisy pixels without significant loss of the fine structure and details that remain in the reconstructed images. The proposed method was evaluated using patient data and actual phantom measurement data acquired with lower mAs. The results show that integrating the RDB further enhances the MI-NLTV denoising-based analytical reconstruction algorithm to achieve a higher CBCT image quality when compared with those generated by NLTV denoising-based approach, with an average of 15.97% higher contrast-to-noise ratio, 2.67% lower root mean square error, 0.12% lower spatial non-uniformity, 1.14% higher correlation, and an average of 18.11% higher detectability index. These quantitative results indicate that the incorporation of MI makes the NLTV more stable and robust than the conventional NLM filter for low-dose CBCT imaging. In addition, achieving clinically acceptable CBCT image quality despite low-mAs projection acquisition can reduce the burden on common online CBCT imaging, improving patient safety throughout the course of radiotherapy.

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“…The structure of the final iterative algorithm becomes a row-action type algorithm concerning both the data-fidelity term and the regularization term, which enables the incorporation of ordered subsets to further accelerate the algorithm 6,7 .…”

Section: Passty Framework Of Cp Algorithmmentioning

confidence: 99%

“…This results in considerable benefits for optimization problems that appear in CT image reconstruction. The structure of the final iterative algorithm becomes a row-action type algorithm concerning both the data-fidelity and regularization terms, which enables the incorporation of ordered subsets to further accelerate the algorithm 2,6,7,9,13 .…”

Section: Introductionmentioning

confidence: 99%

Passty framework of Chambolle-Pock for Fast and High accuracy CT reconstruction

Kim

¹

,

Yu

²

2023

Preprint

Self Cite

0

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To realize low-dose CT, many iterative reconstruction methods have been proposed, but many iterations and high computational complexity are required for each scan. It is necessary to reduce the number of iterations while effectively suppressing artifacts and noise caused by reducing radiation dose for practical use. This study aims to accelerate the algorithm by improving the Chambolle-Pock (CP) algorithm, the latest first-order primal-dual. First, we introduce the Passty framework into the CP algorithm. The proposed CP becomes a row-action type algorithm, enabling the incorporation of ordered subsets to accelerate the algorithm further. Second, to step up with the latest trends, the regularization term (nonlocal TV) is designed with a combined model of the first and second-order derivatives to preserve smooth intensity changes. Third, we extend the proposed CP to the L1 and Huber data-fidelity terms for more practical application. Most CT reconstruction studies employ the L2 data-fidelity term because of its good convergence. Even if the L1 data-fidelity term has tremendous potential in noise immunity, it has a fatal drawback of poor convergence and is not adopted at present. In the proposed CP, the L1 and Huber data-fidelity terms have excellent image quality and convergence performance than the L2.

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Nonlocal Total Variation Using the First and Second Order Derivatives and Its Application to CT image Reconstruction

Cited by 13 publications

References 26 publications

Effect Evaluation of Platelet-Rich Plasma Combined with Vacuum Sealing Drainage on Serum Inflammatory Factors in Patients with Pressure Ulcer by Intelligent Algorithm-Based CT Image

Effect Evaluation of Platelet-Rich Plasma Combined with Vacuum Sealing Drainage on Serum Inflammatory Factors in Patients with Pressure Ulcer by Intelligent Algorithm-Based CT Image

Mutual Information-Based Non-Local Total Variation Denoiser for Low-Dose Cone-Beam Computed Tomography

Passty framework of Chambolle-Pock for Fast and High accuracy CT reconstruction

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