Journal of X-Ray Science and Technology最新文献

{"title":"SADSNet: A robust 3D synchronous segmentation network for liver and liver tumors based on spatial attention mechanism and deep supervision.","authors":"Sijing Yang, Yongbo Liang, Shang Wu, Peng Sun, Zhencheng Chen","doi":"10.3233/XST-230312","DOIUrl":"10.3233/XST-230312","url":null,"abstract":"<p><strong>Highlights: </strong>• Introduce a data augmentation strategy to expand the required different morphological data during the training and learning phase, and improve the algorithm's feature learning ability for complex and diverse tumor morphology CT images.• Design attention mechanisms for encoding and decoding paths to extract fine pixel level features, improve feature extraction capabilities, and achieve efficient spatial channel feature fusion.• The deep supervision layer is used to correct and decode the final image data to provide high accuracy of results.• The effectiveness of this method has been affirmed through validation on the LITS, 3DIRCADb, and SLIVER datasets.</p><p><strong>Background: </strong>Accurately extracting liver and liver tumors from medical images is an important step in lesion localization and diagnosis, surgical planning, and postoperative monitoring. However, the limited number of radiation therapists and a great number of images make this work time-consuming.</p><p><strong>Objective: </strong>This study designs a spatial attention deep supervised network (SADSNet) for simultaneous automatic segmentation of liver and tumors.</p><p><strong>Method: </strong>Firstly, self-designed spatial attention modules are introduced at each layer of the encoder and decoder to extract image features at different scales and resolutions, helping the model better capture liver tumors and fine structures. The designed spatial attention module is implemented through two gate signals related to liver and tumors, as well as changing the size of convolutional kernels; Secondly, deep supervision is added behind the three layers of the decoder to assist the backbone network in feature learning and improve gradient propagation, enhancing robustness.</p><p><strong>Results: </strong>The method was testing on LITS, 3DIRCADb, and SLIVER datasets. For the liver, it obtained dice similarity coefficients of 97.03%, 96.11%, and 97.40%, surface dice of 81.98%, 82.53%, and 86.29%, 95% hausdorff distances of 8.96 mm, 8.26 mm, and 3.79 mm, and average surface distances of 1.54 mm, 1.19 mm, and 0.81 mm. Additionally, it also achieved precise tumor segmentation, which with dice scores of 87.81% and 87.50%, surface dice of 89.63% and 84.26%, 95% hausdorff distance of 12.96 mm and 16.55 mm, and average surface distances of 1.11 mm and 3.04 mm on LITS and 3DIRCADb, respectively.</p><p><strong>Conclusion: </strong>The experimental results show that the proposed method is effective and superior to some other methods. Therefore, this method can provide technical support for liver and liver tumor segmentation in clinical practice.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"707-723"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140327311","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":"Meta analysis of the second course of radiotherapy for recurrent esophageal cancer1.","authors":"Pengcheng Xu, Yongsheng Liu, Shen Wu, Dong Cheng, Zhanfeng Sun","doi":"10.3233/XST-230098","DOIUrl":"10.3233/XST-230098","url":null,"abstract":"<p><strong>Background: </strong>How to improve efficacy and reduce side effects in treating recurrent esophageal cancer by applying the second course of radiotherapy alone and its combination with chemotherapy has been attracting broad research interest.</p><p><strong>Objective: </strong>This review paper aims to systematically evaluate efficacy and side effects of applying the second course of anterograde radiotherapy alone and its combination with chemotherapy in treating recurrent esophageal cancer.</p><p><strong>Methods: </strong>First, the relevant research papers are retrieved from PubMed, CNKI and Wanfang databases. Next, Redman 5.3 software is used to calculate the relative risk and 95% confidence interval to evaluate the efficacy and adverse reactions of applying the single-stage radiotherapy with and without combining single/multi dose chemotherapy to treat recurrent esophageal cancer. Then, a meta data analysis is applied to examine the effectiveness and side effects of radiation alone and re-course radiotherapy plus chemotherapy in treating esophageal cancer recurrence after the first radiotherapy.</p><p><strong>Results: </strong>Fifteen papers are retrieved, which included 956 patients. Among them, 476 patients received radiotherapy combined with single drug/multi drug chemotherapy (observation) and others received only radiotherapy (control). Data analysis results show that the incidence of radiation induced lung injury and bone marrow suppression is high in the observation group. Subgroup analysis also shows the higher effective rate or one-year overall survival rate of patients treated with the second course radiotherapy combined with single drug chemotherapy.</p><p><strong>Conclusion: </strong>The meta-analysis result demonstrates that combining the second course of radiotherapy with single-drug chemotherapy has advantages in treating recurrent esophageal cancer with the manageable side effects. However, due to insufficient data, it is not possible to conduct the further subgroup analysis comparing the side effects of restorative radiation with the combined chemotherapy using between a single drug and multiple drugs.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"141-155"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10894575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9780427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hemi-diaphragm detection of chest X-ray images based on convolutional neural network and graphics.","authors":"Yingjian Yang, Jie Zheng, Peng Guo, Tianqi Wu, Qi Gao, Xueqiang Zeng, Ziran Chen, Nanrong Zeng, Zhanglei Ouyang, Yingwei Guo, Huai Chen","doi":"10.3233/XST-240108","DOIUrl":"10.3233/XST-240108","url":null,"abstract":"<p><strong>Background: </strong>Chest X-rays (CXR) are widely used to facilitate the diagnosis and treatment of critically ill and emergency patients in clinical practice. Accurate hemi-diaphragm detection based on postero-anterior (P-A) CXR images is crucial for the diaphragm function assessment of critically ill and emergency patients to provide precision healthcare for these vulnerable populations.</p><p><strong>Objective: </strong>Therefore, an effective and accurate hemi-diaphragm detection method for P-A CXR images is urgently developed to assess these vulnerable populations' diaphragm function.</p><p><strong>Methods: </strong>Based on the above, this paper proposes an effective hemi-diaphragm detection method for P-A CXR images based on the convolutional neural network (CNN) and graphics. First, we develop a robust and standard CNN model of pathological lungs trained by human P-A CXR images of normal and abnormal cases with multiple lung diseases to extract lung fields from P-A CXR images. Second, we propose a novel localization method of the cardiophrenic angle based on the two-dimensional projection morphology of the left and right lungs by graphics for detecting the hemi-diaphragm.</p><p><strong>Results: </strong>The mean errors of the four key hemi-diaphragm points in the lung field mask images abstracted from static P-A CXR images based on five different segmentation models are 9.05, 7.19, 7.92, 7.27, and 6.73 pixels, respectively. Besides, the results also show that the mean errors of these four key hemi-diaphragm points in the lung field mask images abstracted from dynamic P-A CXR images based on these segmentation models are 5.50, 7.07, 4.43, 4.74, and 6.24 pixels,respectively.</p><p><strong>Conclusion: </strong>Our proposed hemi-diaphragm detection method can effectively perform hemi-diaphragm detection and may become an effective tool to assess these vulnerable populations' diaphragm function for precision healthcare.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1273-1295"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141601990","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":"Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study.","authors":"Haoyan Li, Zhentao Li, Shuaiyi Gao, Jiaqi Hu, Zhihao Yang, Yun Peng, Jihang Sun","doi":"10.3233/XST-230333","DOIUrl":"10.3233/XST-230333","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction-V (ASIR-V) algorithms.</p><p><strong>Methods: </strong>An ACR464 phantom was scanned with DEsCT at four dose levels (3.5 mGy, 5 mGy, 7.5 mGy, and 10 mGy). Virtual monochromatic images were reconstructed at five energy levels (40 keV, 50 keV, 68 keV, 74 keV, and 140 keV) using FBP, 50% and 100% ASIR-V, DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) settings. The noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed and compared among reconstructions.</p><p><strong>Results: </strong>NPS area and noise increased as keV decreased, with DLIR having slower increase than FBP and ASIR-V, and DLIR-H having the lowest values. DLIR had the best 40 keV/140 keV noise ratio at various energy levels, DLIR showed higher TTF (50%) than ASIR-V for all materials, especially for the soft tissue-like polystyrene insert, and DLIR-M and DLIR-H provided higher d' than DLIR-L, ASIR-V and FBP in all dose and energy levels. As keV increases, d' increased for acrylic insert, and d' of the 50 keV DLIR-M and DLIR-H images at 3.5 mGy (7.39 and 8.79, respectively) were higher than that (7.20) of the 50 keV ASIR-V50% images at 10 mGy.</p><p><strong>Conclusions: </strong>DLIR provides better noise containment for low keV images in DEsCT and higher TTF(50%) for the polystyrene insert over ASIR-V. DLIR-H has the lowest image noise and highest detectability in all dose and energy levels. DEsCT 50 keV images with DLIR-M and DLIR-H show potential for 65% dose reduction over ASIR-V50% withhigher d'.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"513-528"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139941066","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":"Application of dose-gradient function in reducing radiation induced lung injury in breast cancer radiotherapy.","authors":"Han Bai, Hui Song, Qianyan Li, Jie Bai, Ru Wang, Xuhong Liu, Feihu Chen, Xiang Pan","doi":"10.3233/XST-230198","DOIUrl":"10.3233/XST-230198","url":null,"abstract":"<p><strong>Objective: </strong>Try to create a dose gradient function (DGF) and test its effectiveness in reducing radiation induced lung injury in breast cancer radiotherapy.</p><p><strong>Materials and methods: </strong>Radiotherapy plans of 30 patients after breast-conserving surgery were included in the study. The dose gradient function was defined as DGH=VDVp3, then the area under the DGF curve of each plan was calculated in rectangular coordinate system, and the minimum area was used as the trigger factor, and other plans were triggered to optimize for area reduction. The dosimetric parameters of target area and organs at risk in 30 cases before and after re-optimization were compared.</p><p><strong>Results: </strong>On the premise of ensuring that the target dose met the clinical requirements, the trigger factor obtained based on DGF could further reduce the V5, V10, V20, V30 and mean lung dose (MLD) of the ipsilateral lung in breast cancer radiotherapy, P < 0.01. And the D2cc and mean heart dose (MHD) of the heart were also reduced, P < 0.01. Besides, the NTCPs of the ipsilateral lung and the heart were also reduced, P < 0.01.</p><p><strong>Conclusion: </strong>The trigger factor obtained based on DGF is efficient in reducing radiation induced lung injury in breast cancer radiotherapy.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"415-426"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11091614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139378671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decomposition iteration strategy for low-dose CT denoising.","authors":"Zhiyuan Li, Yi Liu, Pengcheng Zhang, Jing Lu, Zhiguo Gui","doi":"10.3233/XST-230272","DOIUrl":"10.3233/XST-230272","url":null,"abstract":"<p><p>In the medical field, computed tomography (CT) is a commonly used examination method, but the radiation generated increases the risk of illness in patients. Therefore, low-dose scanning schemes have attracted attention, in which noise reduction is essential. We propose a purposeful and interpretable decomposition iterative network (DISN) for low-dose CT denoising. This method aims to make the network design interpretable and improve the fidelity of details, rather than blindly designing or using deep CNN architecture. The experiment is trained and tested on multiple data sets. The results show that the DISN method can restore the low-dose CT image structure and improve the diagnostic performance when the image details are limited. Compared with other algorithms, DISN has better quantitative and visual performance, and has potential clinical application prospects.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"493-512"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139378673","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":"The mechanism of moire artifacts in single-grating imaging systems and image quality optimization.","authors":"Fangke Zong, Jun Yang, Jun Jiang, JinChuan Guo","doi":"10.3233/XST-230202","DOIUrl":"10.3233/XST-230202","url":null,"abstract":"<p><p>In the X-ray single-grating imaging system, the acquisition of frequency information is the key step of phase-contrast and scattering information recovery. In the process of information extraction, it is easy to lead to the degradation of imaging quality due to the Moire Artifact, thus limiting the development and application of X-ray single-grating imaging system. In order to address the above problems, in this article, based on the theoretical analysis of the generation principle of Moire Artifact in imaging system, the advantages and disadvantages of grating rotation method are analyzed, and a method of suppressing Moire artifacts by adjusting grating projection frequency is proposed. The experimental results show that the method proposed here can suppress the Moire noise in the background noise, resulting in a reduction of more than 50% in the standard deviation of the background noise. High quality phase-contrast and scattering images are obtained experimentally, which is of great value to the development of X-ray single-grating imaging technology.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"461-473"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139378678","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":"Quantifying the effect of X-ray scattering for data generation in real-time defect detection.","authors":"Vladyslav Andriiashen, Robert van Liere, Tristan van Leeuwen, Kees Joost Batenburg","doi":"10.3233/XST-230389","DOIUrl":"10.3233/XST-230389","url":null,"abstract":"<p><strong>Background: </strong>X-ray imaging is widely used for the non-destructive detection of defects in industrial products on a conveyor belt. In-line detection requires highly accurate, robust, and fast algorithms. Deep Convolutional Neural Networks (DCNNs) satisfy these requirements when a large amount of labeled data is available. To overcome the challenge of collecting these data, different methods of X-ray image generation are considered.</p><p><strong>Objective: </strong>Depending on the desired degree of similarity to real data, different physical effects should either be simulated or can be ignored. X-ray scattering is known to be computationally expensive to simulate, and this effect can greatly affect the accuracy of a generated X-ray image. We aim to quantitatively evaluate the effect of scattering on defect detection.</p><p><strong>Methods: </strong>Monte-Carlo simulation is used to generate X-ray scattering distribution. DCNNs are trained on the data with and without scattering and applied to the same test datasets. Probability of Detection (POD) curves are computed to compare their performance, characterized by the size of the smallest detectable defect.</p><p><strong>Results: </strong>We apply the methodology to a model problem of defect detection in cylinders. When trained on data without scattering, DCNNs reliably detect defects larger than 1.3 mm, and using data with scattering improves performance by less than 5%. If the analysis is performed on the cases with large scattering-to-primary ratio (1 < SPR < 5), the difference in performance could reach 15% (approx. 0.4 mm).</p><p><strong>Conclusion: </strong>Excluding the scattering signal from the training data has the largest effect on the smallest detectable defects, and the difference decreases for larger defects. The scattering-to-primary ratio has a significant effect on detection performance and the required accuracy of data generation.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1099-1119"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140873096","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":"Computational fluid dynamics modeling of coronary artery blood flow using OpenFOAM: Validation with the food and drug administration benchmark nozzle model.","authors":"Sajid Ali, Chien-Yi Ho, Chen-Chia Yang, Szu-Hsien Chou, Zhen-Ye Chen, Wei-Chien Huang, Tzu-Ching Shih","doi":"10.3233/XST-230239","DOIUrl":"10.3233/XST-230239","url":null,"abstract":"<p><p>Cardiovascular disease (CVD), a global health concern, particularly coronary artery disease (CAD), poses a significant threat to well-being. Seeking safer and cost-effective diagnostic alternatives to invasive coronary angiography, noninvasive coronary computed tomography angiography (CCTA) gains prominence. This study employed OpenFOAM, an open-source Computational Fluid Dynamics (CFD) software, to analyze hemodynamic parameters in coronary arteries with serial stenoses. Patient-specific three-dimensional (3D) models from CCTA images offer insights into hemodynamic changes. OpenFOAM breaks away from traditional commercial software, validated against the FDA benchmark nozzle model for reliability. Applying this refined methodology to seventeen coronary arteries across nine patients, the study evaluates parameters like fractional flow reserve computed tomography simulation (FFRCTS), fluid velocity, and wall shear stress (WSS) over time. Findings include FFRCTS values exceeding 0.8 for grade 0 stenosis and falling below 0.5 for grade 5 stenosis. Central velocity remains nearly constant for grade 1 stenosis but increases 3.4-fold for grade 5 stenosis. This research innovates by utilizing OpenFOAM, departing from previous reliance on commercial software. Combining qualitative stenosis grading with quantitative FFRCTS and velocity measurements offers a more comprehensive assessment of coronary artery conditions. The study introduces 3D renderings of wall shear stress distribution across stenosis grades, providing an intuitive visualization of hemodynamic changes for valuable insights into coronary stenosis diagnosis.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"1121-1136"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11380260/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141093419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing dual energy X-ray image enhancement using a novel hybrid fusion method.","authors":"Muhammad Fahad, Tao Zhang, Sajid Ullah Khan, Abdullah Albanyan, Fazeela Siddiqui, Yasir Iqbal, Xin Zhao, Yanzhang Geng","doi":"10.3233/XST-240227","DOIUrl":"https://doi.org/10.3233/XST-240227","url":null,"abstract":"<p><strong>Background: </strong>Airport security is still a main concern for assuring passenger safety and stopping illegal activity. Dual-energy X-ray Imaging (DEXI) is one of the most important technologies for detecting hidden items in passenger luggage. However, noise in DEXI images, arising from various sources such as electronic interference and fluctuations in X-ray intensity, can compromise the effectiveness of object identification.</p><p><strong>Objective: </strong>To address the challenge of noise interference in DEXI, this study aims to develop and validate a robust denoising technique using the Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT).</p><p><strong>Methods: </strong>The proposed method targets and removes background and Poisson noise in DEXI images, improving object recognition accuracy. During the denoising process, images are decomposed into several subbands, and thresholding techniques are applied to minimize noise while preserving important information. The images are then reconstructed to provide a cleaner and more accurate depiction of scanned objects.</p><p><strong>Results: </strong>Experimental results demonstrate the effectiveness of the DWT and SWT-based denoising strategy in preserving critical data while suppressing noise in DEXI. The performance of the denoising technique is quantified using Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE). The proposed system achieved an average PSNR of 35.23 and an MSE of 19.52 for 256×256 DEXI images, and an average PSNR of 36.01 and an MSE of 16.29 for 512×512 DEXI images.</p><p><strong>Conclusion: </strong>The results highlight the achievement of the proposed approach in enhancing the quality of DEXI for improved security screening, demonstrating its potential application in airport security systems.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":"32 6","pages":"1553-1570"},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899330","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}