Journal of X-Ray Science and Technology最新文献

{"title":"A reconstruction method for ptychography based on residual dense network.","authors":"Mengnan Liu, Yu Han, Xiaoqi Xi, Lei Li, Zijian Xu, Xiangzhi Zhang, Linlin Zhu, Bin Yan","doi":"10.3233/XST-240114","DOIUrl":"10.3233/XST-240114","url":null,"abstract":"<p><strong>Background: </strong>Coherent diffraction imaging (CDI) is an important lens-free imaging method. As a variant of CDI, ptychography enables the imaging of objects with arbitrary lateral sizes. However, traditional phase retrieval methods are time-consuming for ptychographic imaging of large-size objects, e.g., integrated circuits (IC). Especially when ptychography is combined with computed tomography (CT) or computed laminography (CL), time consumption increases greatly.</p><p><strong>Objective: </strong>In this work, we aim to propose a new deep learning-based approach to implement a quick and robust reconstruction of ptychography.</p><p><strong>Methods: </strong>Inspired by the strong advantages of the residual dense network for computer vision tasks, we propose a dense residual two-branch network (RDenPtycho) based on the ptychography two-branch reconstruction architecture for the fast and robust reconstruction of ptychography. The network relies on the residual dense block to construct mappings from diffraction patterns to amplitudes and phases. In addition, we integrate the physical processes of ptychography into the training of the network to further improve the performance.</p><p><strong>Results: </strong>The proposed RDenPtycho is evaluated using the publicly available ptychography dataset from the Advanced Photon Source. The results show that the proposed method can faithfully and robustly recover the detailed information of the objects. Ablation experiments demonstrate the effectiveness of the components in the proposed method for performance enhancement.</p><p><strong>Significance: </strong>The proposed method enables fast, accurate, and robust reconstruction of ptychography, and is of potential significance for 3D ptychography. The proposed method and experiments can resolve similar problems in other fields.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1505-1519"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A fully linearized ADMM algorithm for optimization based image reconstruction.","authors":"Zhiwei Qiao, Gage Redler, Boris Epel, Howard Halpern","doi":"10.3233/XST-240029","DOIUrl":"10.3233/XST-240029","url":null,"abstract":"<p><strong>Background and objective: </strong>Optimization based image reconstruction algorithm is an advanced algorithm in medical imaging. However, the corresponding solving algorithm is challenging because the model is usually large-scale and non-smooth. This work aims to devise a simple and convergent solver for optimization model.</p><p><strong>Methods: </strong>The alternating direction method of multipliers (ADMM) algorithm is a simple and effective solver of the optimization model. However, there always exists a sub-problem that has not close-form solution. One may use gradient descent algorithm to solve this sub-problem, but the step-size selection via line search is time-consuming. Or, one may use fast Fourier transform (FFT) to get a close-form solution if the sparse transform matrix is of special structure. In this work, we propose a fully linearized ADMM (FL-ADMM) algorithm that avoids line search to determine step-size and applies to sparse transform of any structure.</p><p><strong>Results: </strong>We derive the FL-ADMM algorithm instances for three total variation (TV) models in 2D computed tomography (CT). Further, we validate and evaluate one FL-ADMM algorithm and explore how two important factors impact convergence rate. These studies show that the FL-ADMM algorithm may accurately solve the optimization model.</p><p><strong>Conclusion: </strong>The FL-ADMM algorithm is a simple, effective, convergent and universal solver of optimization model in image reconstruction. Compared to the standard ADMM algorithm, the new algorithm does not need time-consuming step-size line-search or special demand to sparse transform. It is a rapid prototyping tool for optimization based image reconstruction.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1481-1504"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"APNet: Adaptive projection network for medical image denoising.","authors":"Qiyi Song, Xiang Li, Mingbao Zhang, Xiangyi Zhang, Dang N H Thanh","doi":"10.3233/XST-230181","DOIUrl":"10.3233/XST-230181","url":null,"abstract":"<p><strong>Background: </strong>In clinical medicine, low-dose radiographic image noise reduces the quality of the detected image features and may have a negative impact on disease diagnosis.</p><p><strong>Objective: </strong>In this study, Adaptive Projection Network (APNet) is proposed to reduce noise from low-dose medical images.</p><p><strong>Methods: </strong>APNet is developed based on an architecture of the U-shaped network to capture multi-scale data and achieve end-to-end image denoising. To adaptively calibrate important features during information transmission, a residual block of the dual attention method throughout the encoding and decoding phases is integrated. A non-local attention module to separate the noise and texture of the image details by using image adaptive projection during the feature fusion.</p><p><strong>Results: </strong>To verify the effectiveness of APNet, experiments on lung CT images with synthetic noise are performed, and the results demonstrate that the proposed approach outperforms recent methods in both quantitative index and visual quality. In addition, the denoising experiment on the dental CT image is also carried out and it verifies that the network has a certain generalization.</p><p><strong>Conclusions: </strong>The proposed APNet is an effective method that can reduce image noise and preserve the required image details in low-dose radiographic images.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1-15"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71488142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel multi-dimensional coal and gangue X-ray sorting algorithm based on CdZnTe photon counting detectors.","authors":"Yang Kang, Rui Wu, Peizheng Li, Qingpei Li, Sen Wu, Tingting Tan, Yingrui Li, Gangqiang Zha","doi":"10.3233/XST-230250","DOIUrl":"10.3233/XST-230250","url":null,"abstract":"<p><strong>Background: </strong>The gangue content in coal seriously affects the calorific value produced by its combustion. In practical applications, gangue in coal needs to be completely separated. The pseudo-dual-energy X-ray method does not have high sorting accuracy.</p><p><strong>Objective: </strong>This study aims to propose a novel multi-dimensional coal and gangue X-ray sorting algorithm based on CdZnTe photon counting detectors to solve the problem of coal and gangue sorting by X-ray.</p><p><strong>Methods: </strong>This complete algorithm includes five steps: (1) Preferred energy bins, (2) transmittance sorting, (3) one-dimensional R-value sorting, (4) two-dimensional R-value sorting, and (5) three-dimensional R-value sorting. The output range of each step is determined by prior information from 65 groups of coal and gangue. An additional 110 groups of coal and gangue are employed experimentally to validate the algorithm's accuracy.</p><p><strong>Results: </strong>Compared with the 60% sorting accuracy of the Pseudo-dual-energy method, the new algorithm reached a sorting accuracy of 99%.</p><p><strong>Conclusions: </strong>Study results demonstrate the superiority of this novel algorithm and its feasibility in practical applications. This novel algorithm can guide other two-substance X-ray sorting applications based on photon counting detectors.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"369-378"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139378670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Approaches for Stereotactic Radiosurgery (SRS)/Stereotactic Radiotherapy (SRT) in brain metastases using different radiotherapy modalities (Feasibility study).","authors":"Zyad A Tawfik, Mohamed El-Azab Farid, Khaled M El Shahat, Ahmed A Hussein, Mostafa Al Etreby","doi":"10.3233/XST-230275","DOIUrl":"10.3233/XST-230275","url":null,"abstract":"<p><strong>Background: </strong>SRS and SRT are precise treatments for brain metastases, delivering high doses while minimizing doses to nearby organs. Modern linear accelerators enable the precise delivery of SRS/SRT using different modalities like three-dimensional conformal radiotherapy (3DCRT), intensity-modulated radiotherapy (IMRT), and Rapid Arc (RA).</p><p><strong>Objective: </strong>This study aims to compare dosimetric differences and evaluate the effectiveness of 3DCRT, IMRT, and Rapid Arc techniques in SRS/SRT for brain metastases.</p><p><strong>Methods: </strong>10 patients with brain metastases, 3 patients assigned for SRT, and 7 patients for SRS. For each patient, 3 treatment plans were generated using the Eclipse treatment planning system using different treatment modalities.</p><p><strong>Results: </strong>No statistically significant differences were observed among the three techniques in the homogeneity index (HI), maximum D2%, and minimum D98% doses for the target, with a p > 0.05. The RA demonstrated a better conformity index of 1.14±0.25 than both IMRT 1.21±0.26 and 3DCRT 1.37±0.31. 3DCRT and IMRT had lower Gradient Index values compared to RA, suggesting that they achieved a better dose gradient than RA. The mean treatment time decreased by 26.2% and 10.3% for 3DCRT and RA, respectively, compared to IMRT. In organs at risk, 3DCRT had lower maximum doses than IMRT and RA, but some differences were not statistically significant. However, in the brain stem and brain tissues, RA exhibited lower maximum doses compared to IMRT and 3DCRT. Additionally, RA and IMRT had lower V15Gy, V12Gy, and V9Gy values compared to 3DCRT.</p><p><strong>Conclusion: </strong>While 3D-CRT delivered lower doses to organs at risk, RA and IMRT provided better conformity and target coverage. RA effectively controlled the maximum dose and irradiated volume of normal brain tissue. Overall, these findings indicate that 3DCRT, RA, and IMRT are suitable for treating brain metastases in SRS/SRT due to their improved dose conformity and target coverage while minimizing dose to healthy tissues.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"765-781"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139566099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An improved attention module based on nnU-Net for segmenting primary central nervous system lymphoma (PCNSL) in MRI images1.","authors":"Chen Zhao, Jianping Song, Yifan Yuan, Ying-Hua Chu, Yi-Cheng Hsu, Qiu Huang","doi":"10.3233/XST-240016","DOIUrl":"10.3233/XST-240016","url":null,"abstract":"<p><strong>Background: </strong>Accurate volumetric segmentation of primary central nervous system lymphoma (PCNSL) is essential for assessing and monitoring the tumor before radiotherapy and the treatment planning. The tedious manual segmentation leads to interindividual and intraindividual differences, while existing automatic segmentation methods cause under-segmentation of PCNSL due to the complex and multifaceted nature of the tumor.</p><p><strong>Objective: </strong>To address the challenges of small size, diffused distribution, poor inter-layer continuity on the same axis, and tendency for over-segmentation in brain MRI PCNSL segmentation, we propose an improved attention module based on nnUNet for automated segmentation.</p><p><strong>Methods: </strong>We collected 114 T1 MRI images of patients in the Huashan Hospital, Shanghai. Then randomly split the total of 114 cases into 5 distinct training and test sets for a 5-fold cross-validation. To efficiently and accurately delineate the PCNSL, we proposed an improved attention module based on nnU-Net with 3D convolutions, batch normalization, and residual attention (res-attention) to learn the tumor region information. Additionally, multi-scale dilated convolution kernels with different dilation rates were integrated to broaden the receptive field. We further used attentional feature fusion with 3D convolutions (AFF3D) to fuse the feature maps generated by multi-scale dilated convolution kernels to reduce under-segmentation.</p><p><strong>Results: </strong>Compared to existing methods, our attention module improves the ability to distinguish diffuse and edge enhanced types of tumors; and the broadened receptive field captures tumor features of various scales and shapes more effectively, achieving a 0.9349 Dice Similarity Coefficient (DSC).</p><p><strong>Conclusions: </strong>Quantitative results demonstrate the effectiveness of the proposed method in segmenting the PCNSL. To our knowledge, this is the first study to introduce attention modules into deep learning for segmenting PCNSL based on brain magnetic resonance imaging (MRI), promoting the localization of PCNSL before radiotherapy.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"993-1009"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140905197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lossless compression-based detection of osteoporosis using bone X-ray imaging.","authors":"Khalaf Alshamrani, Hassan A Alshamrani","doi":"10.3233/XST-230238","DOIUrl":"10.3233/XST-230238","url":null,"abstract":"<p><strong>Background: </strong>Digital X-ray imaging is essential for diagnosing osteoporosis, but distinguishing affected patients from healthy individuals using these images remains challenging.</p><p><strong>Objective: </strong>This study introduces a novel method using deep learning to improve osteoporosis diagnosis from bone X-ray images.</p><p><strong>Methods: </strong>A dataset of bone X-ray images was analyzed using a newly proposed procedure. This procedure involves segregating the images into regions of interest (ROI) and non-ROI, thereby reducing data redundancy. The images were then processed to enhance both spatial and statistical features. For classification, a Support Vector Machine (SVM) classifier was employed to distinguish between osteoporotic and non-osteoporotic cases.</p><p><strong>Results: </strong>The proposed method demonstrated a promising Area under the Curve (AUC) of 90.8% in diagnosing osteoporosis, benchmarking favorably against existing techniques. This signifies a high level of accuracy in distinguishing osteoporosis patients from healthy controls.</p><p><strong>Conclusions: </strong>The proposed method effectively distinguishes between osteoporotic and non-osteoporotic cases using bone X-ray images. By enhancing image features and employing SVM classification, the technique offers a promising tool for efficient and accurate osteoporosis diagnosis.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"475-491"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139941064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extracellular volume fraction of liver and pancreas using spectral CT in hypertensive patients: A comparative study.","authors":"Xiaoming Huang, Zhen Zhang, Jiansheng Wang, Yaqing Yang, Tianqi Hao, Shuai Zhang, Ling Liu, Guohua Wang","doi":"10.3233/XST-240130","DOIUrl":"10.3233/XST-240130","url":null,"abstract":"<p><strong>Background: </strong>Besides the direct impact on the cardiovascular system, hypertension is closely associated with organ damage in the kidneys, liver, and pancreas. Chronic liver and pancreatic damage in hypertensive patients may be detectable via imaging.</p><p><strong>Objective: </strong>To explore the correlation between hypertension-related indicators and extracellular volume fraction (ECV) of liver and pancreas measured by iodine maps, and to evaluate corresponding clinical value in chronic damage of liver and pancreas in hypertensive patients.</p><p><strong>Methods: </strong>A prospective study from June to September 2023 included abdominal patients who underwent contrast-enhanced spectral CT. Normal and various grades of hypertensive blood pressure groups were compared. Upper abdominal iodine maps were constructed, and liver and pancreatic ECVs calculated. Kruskal-Wallis and Spearman analyses evaluated ECV differences and correlations with hypertension indicators.</p><p><strong>Results: </strong>In 300 patients, hypertensive groups showed significantly higher liver and pancreatic ECV than the normotensive group, with ECV rising alongside hypertension severity. ECVliver displayed a stronger correlation with hypertension stages compared to ECVpancreas. Regression analysis identified hypertension severity as an independent predictor for increased ECV.</p><p><strong>Conclusions: </strong>ECVliver and ECVpancreas positively correlates with hypertension indicators and serves as a potential clinical marker for chronic organ damage due to hypertension, with ECVliver being more strongly associated than ECVpancreas.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1351-1362"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility study of YSO/SiPM based detectors for virtual monochromatic image synthesis.","authors":"Du Zhang, Bin Wu, Daoming Xi, Rui Chen, Peng Xiao, Qingguo Xie","doi":"10.3233/XST-240039","DOIUrl":"10.3233/XST-240039","url":null,"abstract":"<p><strong>Background: </strong>The development of photon-counting CT systems has focused on semiconductor detectors like cadmium zinc telluride (CZT) and cadmium telluride (CdTe). However, these detectors face high costs and charge-sharing issues, distorting the energy spectrum. Indirect detection using Yttrium Orthosilicate (YSO) scintillators with silicon photomultiplier (SiPM) offers a cost-effective alternative with high detection efficiency, low dark count rate, and high sensor gain.</p><p><strong>Objective: </strong>This work aims to demonstrate the feasibility of the YSO/SiPM detector (DexScanner L103) based on the Multi-Voltage Threshold (MVT) sampling method as a photon-counting CT detector by evaluating the synthesis error of virtual monochromatic images.</p><p><strong>Methods: </strong>In this study, we developed a proof-of-concept benchtop photon-counting CT system, and employed a direct method for empirical virtual monochromatic image synthesis (EVMIS) by polynomial fitting under the principle of least square deviation without X-ray spectral information. The accuracy of the empirical energy calibration techniques was evaluated by comparing the reconstructed and actual attenuation coefficients of calibration and test materials using mean relative error (MRE) and mean square error (MSE).</p><p><strong>Results: </strong>In dual-material imaging experiments, the overall average synthesis error for three monoenergetic images of distinct materials is 2.53% ±2.43%. Similarly, in K-edge imaging experiments encompassing four materials, the overall average synthesis error for three monoenergetic images is 4.04% ±2.63%. In rat biological soft-tissue imaging experiments, we further predicted the densities of various rat tissues as follows: bone density is 1.41±0.07 g/cm3, adipose tissue density is 0.91±0.06 g/cm3, heart tissue density is 1.09±0.04 g/cm3, and lung tissue density is 0.32±0.07 g/cm3. Those results showed that the reconstructed virtual monochromatic images had good conformance for each material.</p><p><strong>Conclusion: </strong>This study indicates the SiPM-based photon-counting detector could be used for monochromatic image synthesis and is a promising method for developing spectral computed tomography systems.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1363-1383"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142373368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiobjective optimization guided by image quality index for limited-angle CT image reconstruction.","authors":"Yu He, Chengxiang Wang, Wei Yu, Jiaxi Wang","doi":"10.3233/XST-240111","DOIUrl":"10.3233/XST-240111","url":null,"abstract":"<p><strong>Background: </strong>Due to the incomplete projection data collected by limited-angle computed tomography (CT), severe artifacts are present in the reconstructed image. Classical regularization methods such as total variation (TV) minimization, ℓ0 minimization, are unable to suppress artifacts at the edges perfectly. Most existing regularization methods are single-objective optimization approaches, stemming from scalarization methods for multiobjective optimization problems (MOP).</p><p><strong>Objective: </strong>To further suppress the artifacts and effectively preserve the edge structures of the reconstructed image.</p><p><strong>Method: </strong>This study presents a multiobjective optimization model incorporates both data fidelity term and ℓ0-norm of the image gradient as objective functions. It employs an iterative approach different from traditional scalarization methods, using the maximization of structural similarity (SSIM) values to guide optimization rather than minimizing the objective function.The iterative method involves two steps, firstly, simultaneous algebraic reconstruction technique (SART) optimizes the data fidelity term using SSIM and the Simulated Annealing (SA) algorithm for guidance. The degradation solution is accepted in the form of probability, and guided image filtering (GIF) is introduced to further preserve the image edge when the degradation solution is rejected. Secondly, the result from the first step is integrated into the second objective function as a constraint, we use ℓ0 minimization to optimize ℓ0-norm of the image gradient, and the SSIM, SA algorithm and GIF are introduced to guide optimization process by improving SSIM value like the first step.</p><p><strong>Results: </strong>With visual inspection, the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and SSIM values indicate that our approach outperforms other traditional methods.</p><p><strong>Conclusions: </strong>The experiments demonstrate the effectiveness of our method and its superiority over other classical methods in artifact suppression and edge detail restoration.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1209-1237"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141601991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}