Journal of X-Ray Science and Technology - Volume 32, issue 2

Authors: Tanveer, Md Sayed | Wiedeman, Christopher | Li, Mengzhou | Shi, Yongyi | De Man, Bruno | Maltz, Jonathan S. | Wang, Ge

Article Type: Research Article

Abstract: BACKGROUND: In recent years, deep reinforcement learning (RL) has been applied to various medical tasks and produced encouraging results. OBJECTIVE: In this paper, we demonstrate the feasibility of deep RL for denoising simulated deep-silicon photon-counting CT (PCCT) data in both full and interior scan modes. PCCT offers higher spatial and spectral resolution than conventional CT, requiring advanced denoising methods to suppress noise increase. METHODS: In this work, we apply a dueling double deep Q network (DDDQN) to denoise PCCT data for maximum contrast-to-noise ratio (CNR) and a multi-agent approach to handle data non-stationarity. RESULTS: …Using our method, we obtained significant image quality improvement for single-channel scans and consistent improvement for all three channels of multichannel scans. For the single-channel interior scans, the PSNR (dB) and SSIM increased from 33.4078 and 0.9165 to 37.4167 and 0.9790 respectively. For the multichannel interior scans, the channel-wise PSNR (dB) increased from 31.2348, 30.7114, and 30.4667 to 31.6182, 30.9783, and 30.8427 respectively. Similarly, the SSIM improved from 0.9415, 0.9445, and 0.9336 to 0.9504, 0.9493, and 0.0326 respectively. CONCLUSIONS: Our results show that the RL approach improves image quality effectively, efficiently, and consistently across multiple spectral channels and has great potential in clinical applications. Show more

Keywords: Photon-counting CT, deep-silicon detector, projection denoising, artificial intelligence, neural network, deep reinforcement learning, multi-agent learning

DOI: 10.3233/XST-230278

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 173-205, 2024

Authors: Liu, Peng | Fang, Chenyun | Qiao, Zhiwei

Article Type: Research Article

Abstract: OBJECTIVE: CT image reconstruction from sparse-view projections is an important imaging configuration for low-dose CT, as it can reduce radiation dose. However, the CT images reconstructed from sparse-view projections by traditional analytic algorithms suffer from severe sparse artifacts. Therefore, it is of great value to develop advanced methods to suppress these artifacts. In this work, we aim to use a deep learning (DL)-based method to suppress sparse artifacts. METHODS: Inspired by the good performance of DenseNet and Transformer architecture in computer vision tasks, we propose a Dense U-shaped Transformer (D-U-Transformer) to suppress sparse artifacts. This architecture exploits the …advantages of densely connected convolutions in capturing local context and Transformer in modelling long-range dependencies, and applies channel attention to fusion features. Moreover, we design a dual-domain multi-loss function with learned weights for the optimization of the model to further improve image quality. RESULTS: Experimental results of our proposed D-U-Transformer yield performance improvements on the well-known Mayo Clinic LDCT dataset over several representative DL-based models in terms of artifact suppression and image feature preservation. Extensive internal ablation experiments demonstrate the effectiveness of the components in the proposed model for sparse-view computed tomography (SVCT) reconstruction. SIGNIFICANCE: The proposed method can effectively suppress sparse artifacts and achieve high-precision SVCT reconstruction, thus promoting clinical CT scanning towards low-dose radiation and high-quality imaging. The findings of this work can be applied to denoising and artifact removal tasks in CT and other medical images. Show more

Keywords: Computed tomography, sparse-view reconstruction, deep convolutional network, Transformer, multi-loss function

DOI: 10.3233/XST-230184

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 207-228, 2024

Authors: Ren, Junru | Zhang, Wenkun | Wang, YiZhong | Liang, Ningning | Wang, Linyuan | Cai, Ailong | Wang, Shaoyu | Zheng, Zhizhong | Li, Lei | Yan, Bin

Article Type: Research Article

Abstract: Compared with conventional single-energy computed tomography (CT), dual-energy CT (DECT) provides better material differentiation but most DECT imaging systems require dual full-angle projection data at different X-ray spectra. Relaxing the requirement of data acquisition is an attractive research to promote the applications of DECT in wide range areas and reduce the radiation dose as low as reasonably achievable. In this work, we design a novel DECT imaging scheme with dual quarter scans and propose an efficient method to reconstruct the desired DECT images from the dual limited-angle projection data. We first study the characteristics of limited-angle artifacts under dual quarter …scans scheme, and find that the negative and positive artifacts of DECT images are complementarily distributed in image domain because the corresponding X-rays of high- and low-energy scans are symmetric. Inspired by this finding, a fusion CT image is generated by integrating the limited-angle DECT images of dual quarter scans. This strategy enhances the true image information and suppresses the limited-angle artifacts, thereby restoring the image edges and inner structures. Utilizing the capability of neural network in the modeling of nonlinear problem, a novel Anchor network with single-entry double-out architecture is designed in this work to yield the desired DECT images from the generated fusion CT image. Experimental results on the simulated and real data verify the effectiveness of the proposed method. This work enables DECT on imaging configurations with half-scan and largely reduces scanning angles and radiation doses. Show more

Keywords: Dual-energy CT, dual quarter scans, limited-angle problem, characteristic analysis, anchor network

DOI: 10.3233/XST-230245

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 229-252, 2024

Authors: Betshrine Rachel, R. | Khanna Nehemiah, H. | Singh, Vaibhav Kumar | Manoharan, Rebecca Mercy Victoria

Article Type: Research Article

Abstract: BACKGROUND: The coronavirus disease 2019 is a serious and highly contagious disease caused by infection with a newly discovered virus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). OBJECTIVE: A Computer Aided Diagnosis (CAD) system to assist physicians to diagnose Covid-19 from chest Computed Tomography (CT) slices is modelled and experimented. METHODS: The lung tissues are segmented using Otsu’s thresholding method. The Covid-19 lesions have been annotated as the Regions of Interest (ROIs), which is followed by texture and shape extraction. The obtained features are stored as feature vectors and split into 80:20 train and test …sets. To choose the optimal features, Whale Optimization Algorithm (WOA) with Support Vector Machine (SVM) classifier’s accuracy is employed. A Multi-Layer Perceptron (MLP) classifier is trained to perform classification with the selected features. RESULTS: Comparative experimentations of the proposed system with existing eight benchmark Machine Learning classifiers using real-time dataset demonstrates that the proposed system with 88.94% accuracy outperforms the benchmark classifier’s results. Statistical analysis namely, Friedman test, Mann Whitney U test and Kendall’s Rank Correlation Coefficient Test has been performed which indicates that the proposed method has a significant impact on the novel dataset considered. CONCLUSION: The MLP classifier’s accuracy without feature selection yielded 80.40%, whereas with feature selection using WOA, it yielded 88.94%. Show more

Keywords: Covid-19, WOA, SVM, MLP, kendall’s correlation coefficient graph

DOI: 10.3233/XST-230196

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 253-269, 2024

Authors: Jiang, Shi Bo | Sun, Yue Wen | Xu, Shuo | Zhang, Hua Xia | Wu, Zhi Fang

Article Type: Research Article

Abstract: Accurate segmentation of industrial CT images is of great significance in industrial fields such as quality inspection and defect analysis. However, reconstruction of industrial CT images often suffers from typical metal artifacts caused by factors like beam hardening, scattering, statistical noise, and partial volume effects. Traditional segmentation methods are difficult to achieve precise segmentation of CT images mainly due to the presence of these metal artifacts. Furthermore, acquiring paired CT image data required by fully supervised networks proves to be extremely challenging. To address these issues, this paper introduces an improved CycleGAN approach for achieving semi-supervised segmentation of industrial CT …images. This method not only eliminates the need for removing metal artifacts and noise, but also enables the direct conversion of metal artifact-contaminated images into segmented images without the requirement of paired data. The average values of quantitative assessment of image segmentation performance can reach 0.96645 for Dice Similarity Coefficient(Dice) and 0.93718 for Intersection over Union(IoU). In comparison to traditional segmentation methods, it presents significant improvements in both quantitative metrics and visual quality, provides valuable insights for further research. Show more

Keywords: Industrial CT, image segmentation, metal artifact, CycleGAN, dataset acquisition, semi-supervised

DOI: 10.3233/XST-230233

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 271-283, 2024

Authors: Wu, Panpan | Qu, Yue | Zhao, Ziping | Cui, Yue | Xu, Yurou | An, Peng | Yu, Hengyong

Article Type: Research Article

Abstract: Diabetic retinopathy (DR) is one of the leading causes of blindness. However, because the data distribution of classes is not always balanced, it is challenging for automated early DR detection using deep learning techniques. In this paper, we propose an adaptive weighted ensemble learning method for DR detection based on optical coherence tomography (OCT) images. Specifically, we develop an ensemble learning model based on three advanced deep learning models for higher performance. To better utilize the cues implied in these base models, a novel decision fusion scheme is proposed based on the Bayesian theory in terms of the key evaluation …indicators, to dynamically adjust the weighting distribution of base models to alleviate the negative effects potentially caused by the problem of unbalanced data size. Extensive experiments are performed on two public datasets to verify the effectiveness of the proposed method. A quadratic weighted kappa of 0.8487 and an accuracy of 0.9343 on the DRAC2022 dataset, and a quadratic weighted kappa of 0.9007 and an accuracy of 0.8956 on the APTOS2019 dataset are obtained, respectively. The results demonstrate that our method has the ability to enhance the ovearall performance of DR detection on OCT images. Show more

Keywords: Diabetic retinopathy, ensemble learning, decision fusion

DOI: 10.3233/XST-230252

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 285-301, 2024

Authors: Hsieh, Shang-Ting | Cheng, Ya-Ai

Article Type: Research Article

Abstract: BACKGROUND: Dental health issues are on the rise, necessitating prompt and precise diagnosis. Automated dental condition classification can support this need. OBJECTIVE: The study aims to evaluate the effectiveness of deep learning methods and multimodal feature fusion techniques in advancing the field of automated dental condition classification. METHODS AND MATERIALS: A dataset of 11,653 clinically sourced images representing six prevalent dental conditions—caries, calculus, gingivitis, tooth discoloration, ulcers, and hypodontia—was utilized. Features were extracted using five Convolutional Neural Network (CNN) models, then fused into a matrix. Classification models were constructed using Support Vector Machines …(SVM) and Naive Bayes classifiers. Evaluation metrics included accuracy, recall rate, precision, and Kappa index. RESULTS: The SVM classifier integrated with feature fusion demonstrated superior performance with a Kappa index of 0.909 and accuracy of 0.925. This significantly surpassed individual CNN models such as EfficientNetB0, which achieved a Kappa of 0.814 and accuracy of 0.847. CONCLUSIONS: The amalgamation of feature fusion with advanced machine learning algorithms can significantly bolster the precision and robustness of dental condition classification systems. Such a method presents a valuable tool for dental professionals, facilitating enhanced diagnostic accuracy and subsequently improved patient outcomes. Show more

Keywords: Dental conditions, deep learning, convolutional neural network, multimodal feature fusion, SVM

DOI: 10.3233/XST-230271

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 303-321, 2024

Authors: Lai, Yexin | Liu, Xueyu | Hou, Fan | Han, Zhiyong | E, Linning | Su, Ningling | Du, Dianrong | Wang, Zhichong | Zheng, Wen | Wu, Yongfei

Article Type: Research Article

Abstract: BACKGROUND: Interstitial lung disease (ILD) represents a group of chronic heterogeneous diseases, and current clinical practice in assessment of ILD severity and progression mainly rely on the radiologist-based visual screening, which greatly restricts the accuracy of disease assessment due to the high inter- and intra-subjective observer variability. OBJECTIVE: To solve these problems, in this work, we propose a deep learning driven framework that can assess and quantify lesion indicators and outcome the prediction of severity of ILD. METHODS: In detail, we first present a convolutional neural network that can segment and quantify five …types of lesions including HC, RO, GGO, CONS, and EMPH from HRCT of ILD patients, and then we conduct quantitative analysis to select the features related to ILD based on the segmented lesions and clinical data. Finally, a multivariate prediction model based on nomogram to predict the severity of ILD is established by combining multiple typical lesions. RESULTS: Experimental results showed that three lesions of HC, RO, and GGO could accurately predict ILD staging independently or combined with other HRCT features. Based on the HRCT, the used multivariate model can achieve the highest AUC value of 0.755 for HC, and the lowest AUC value of 0.701 for RO in stage I, and obtain the highest AUC value of 0.803 for HC, and the lowest AUC value of 0.733 for RO in stage II. Additionally, our ILD scoring model could achieve an average accuracy of 0.812 (0.736 - 0.888) in predicting the severity of ILD via cross-validation. CONCLUSIONS: In summary, our proposed method provides effective segmentation of ILD lesions by a comprehensive deep-learning approach and confirms its potential effectiveness in improving diagnostic accuracy for clinicians. Show more

Keywords: Interstitial lung disease, deep learning, lesion quantification, severity prediction

DOI: 10.3233/XST-230218

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 323-338, 2024

Authors: Chang, Jiahao | Zhu, Chaoyang | Song, Yuanpeng | Wang, Zhentao

Article Type: Research Article

Abstract: The time response characteristic of the detector is crucial in radiation imaging systems. Unfortunately, existing parallel plate ionization chamber detectors have a slow response time, which leads to blurry radiation images. To enhance imaging quality, the electrode structure of the detector must be modified to reduce the response time. This paper proposes a gas detector with a grid structure that has a fast response time. In this study, the detector electrostatic field was calculated using COMSOL, while Garfield++ was utilized to simulate the detector’s output signal. To validate the accuracy of simulation results, the experimental ionization chamber was tested on …the experimental platform. The results revealed that the average electric field intensity in the induced region of the grid detector was increased by at least 33%. The detector response time was reduced to 27% –38% of that of the parallel plate detector, while the sensitivity of the detector was only reduced by 10%. Therefore, incorporating a grid structure within the parallel plate detector can significantly improve the time response characteristics of the gas detector, providing an insight for future detector enhancements. Show more

Keywords: Time response characteristics, gas ionization chamber, garfield++, grid detector, detector sensitivity

DOI: 10.3233/XST-230219

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 339-354, 2024

Authors: Pérez, MartÍn | Lado, Gerardo M. | Mato, Germán | Franco, Diego G. | Vinciguerra, Ignacio Artola | Berisso, Mariano Gómez | Pomiro, Federico J. | Lipovetzky, José | Marpegan, Luciano

Article Type: Research Article

Abstract:  An automated system for acquiring microscopic-resolution radiographic images of biological samples was developed. Mass-produced, low-cost, and easily automated components were used, such as Commercial-Off-The-Self CMOS image sensors (CIS), stepper motors, and control boards based on Arduino and RaspberryPi. System configuration, imaging protocols, and Image processing (filtering and stitching) were defined to obtain high-resolution images and for successful computational image reconstruction. Radiographic images were obtained for animal samples including the widely used animal models zebrafish (Danio rerio ) and the fruit-fly (Drosophila melanogaster ), as well as other small animal samples. The use of phosphotungstic acid (PTA) as a contrast agent …was also studied. Radiographic images with resolutions of up to (7±0.6)μm were obtained, making this system comparable to commercial ones. This work constitutes a starting point for the development of more complex systems such as X-ray attenuation micro-tomography systems based on low-cost off-the-shelf technology. It will also bring the possibility to expand the studies that can be carried out with small animal models at many institutions (mostly those working on tight budgets), particularly those on the effects of ionizing radiation and absorption of heavy metal contaminants in animal tissues. Show more

Keywords: Zebrafish, Drosophila, CMOS, image processing, image sensors, X-ray imaging

DOI: 10.3233/XST-230232

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 355-367, 2024

Authors: Kang, Yang | Wu, Rui | Li, Peizheng | Li, Qingpei | Wu, Sen | Tan, Tingting | Li, Yingrui | Zha, Gangqiang

Article Type: Research Article

Abstract: BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: Compared with the 60% sorting accuracy of the Pseudo-dual-energy method, the new algorithm reached a sorting accuracy of 99%. CONCLUSIONS: 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. Show more

Keywords: X-ray, sorting algorithm, photon counting, CdZnTe detector

DOI: 10.3233/XST-230250

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 369-378, 2024

Authors: Duan, Yanhua | Feng, Aihui | Wang, Hao | Chen, Hua | Gu, Hengle | Shao, Yan | Huang, Ying | Shen, Zhenjiong | Kong, Qing | Xu, Zhiyong

Article Type: Research Article

Abstract: Purpose: This study aims to assess the dosimetry and treatment efficiency of TaiChiB-based Stereotactic Body Radiotherapy (SBRT) plans applying to treat two-lung lesions with one overlapping organs at risk. Methods: For four retrospective patients diagnosed with two-lung lesions each patient, four treatment plans were designed including Plan Edge , TaiChiB linac-based, RGS-based, and a linac-RGS hybrid (Plan TCLinac , Plan TCRGS , and Plan TCHybrid ). Dosimetric metrics and beam-on time were employed to evaluate and compare the TaiChiB-based plans against Plan Edge . Results: For Conformity Index (CI), Plan TCRGS outperformed all other …plans with an average CI of 1.06, as opposed to Plan Edge′ s 1.33. Similarly, for R50 % , Plan TCRGS was superior with an average R50 % of 3.79, better than Plan Edge′ s 4.28. In terms of D2  cm , Plan TCRGS also led with an average of 48.48%, compared to Plan Edge′ s 56.25%. For organ at risk (OAR) sparing, Plan TCRGS often displayed the lowest dosimetric values, notably for the spinal cord (Dmax 5.92 Gy) and lungs (D1500cc 1.00 Gy, D1000cc 2.61 Gy, V10  Gy 15.14%). However, its high Dmax values for the heart and great vessels sometimes exceeded safety thresholds. Plan TCHybrid presented a balanced approach, showing doses comparable to or better than Plan Edge without crossing safety limits. In terms of beam-on time, Plan TCLinac emerged as the most efficient treatment option in three out of four cases, followed closely by Plan Edge in one case. Plan TCRGS , despite its dosimetric advantages, was the least efficient, recording notably longer beam-on times, with a peak at 33.28 minutes in Case 2. Conclusion: For patients with two-lung lesions treated by SBRT whose one lesion overlaps with OARs, the Plan TCHybrid delivered by TaiChiB digital radiotherapy system can be recommended as a clinical option. Show more

Keywords: SBRT, TaiChiB, two-target lung lesions, rotating gamma system, Edge linac, hybrid plan

DOI: 10.3233/XST-230176

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 379-394, 2024

Authors: Li, Wenxiu | Gou, Fangfang | Wu, Jia

Article Type: Research Article

Abstract: BACKGROUND: In many developing countries, a significant number of breast cancer patients are unable to receive timely treatment due to a large population base, high patient numbers, and limited medical resources. OBJECTIVE: This paper proposes a breast cancer assisted diagnosis system based on electronic medical records. The goal of this system is to address the limitations of existing systems, which primarily rely on structured electronic records and may miss crucial information stored in unstructured records. METHODS: The proposed approach is a breast cancer assisted diagnosis system based on electronic medical records. The system utilizes breast cancer …enhanced convolutional neural networks with semantic initialization filters (BC-INIT-CNN). It extracts highly relevant tumor markers from unstructured medical records to aid in breast cancer staging diagnosis and effectively utilizes the important information present in unstructured records. RESULTS: The model’s performance is assessed using various evaluation metrics. Such as accuracy, ROC curves, and Precision-Recall curves. Comparative analysis demonstrates that the BC-INIT-CNN model outperforms several existing methods in terms of accuracy and computational efficiency. CONCLUSIONS: The proposed breast cancer assisted diagnosis system based on BC-INIT-CNN showcases the potential to address the challenges faced by developing countries in providing timely treatment to breast cancer patients. By leveraging unstructured medical records and extracting relevant tumor markers, the system enables accurate staging diagnosis and enhances the utilization of valuable information. Show more

Keywords: Breast cancer, cancer staging, auxiliary diagnosis, attention mechanism, convolutional neural network

DOI: 10.3233/XST-230194

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 395-413, 2024

Authors: Bai, Han | Song, Hui | Li, Qianyan | Bai, Jie | Wang, Ru | Liu, Xuhong | Chen, Feihu | Pan, Xiang

Article Type: Research Article

Abstract: OBJECTIVE: Try to create a dose gradient function (DGF) and test its effectiveness in reducing radiation induced lung injury in breast cancer radiotherapy. MATERIALS AND METHODS: Radiotherapy plans of 30 patients after breast-conserving surgery were included in the study. The dose gradient function was defined as D G H = V D V p 3 , 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. RESULTS: 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. CONCLUSION: The trigger factor obtained based on DGF is efficient in reducing radiation induced lung injury in breast cancer radiotherapy. Show more

Keywords: Radiation induced lung injury, dose gradient function, trigger factor, re-optimization, breast-conserving surgery

DOI: 10.3233/XST-230198

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 415-426, 2024

Authors: Fu, Baoyue | Wei, Longyu | Wang, Chuanbin | Xiong, Baizhu | Bo, Juan | Jiang, Xueyan | Zhang, Yu | Jia, Haodong | Dong, Jiangning

Article Type: Research Article

Abstract: OBJECTIVE: To explore the value of body composition changes (BCC) measured by quantitative computed tomography (QCT) for evaluating the survival of patients with locally advanced cervical cancer (LACC) underwent concurrent chemoradiotherapy (CCRT), nomograms combined BCC with clinical prognostic factors (CPF) were constructed to predict overall survival (OS) and progression-free survival (PFS). METHODS: Eighty-eight patients with LACC were retrospectively selected. All patients underwent QCT scans before and after CCRT, bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA) were measured from two sets of computed tomography (CT) images, …and change rates of these were calculated. RESULTS: Multivariate Cox regression analysis showed Δ BMD, Δ SFA, SCC-Ag, LNM were independent factors for OS (HR = 3.560, 5.870, 2.702, 2.499, respectively, all P  < 0.05); Δ PMA, SCC-Ag, LNM were independent factors for PFS (HR = 2.915, 4.291, 2.902, respectively, all P  < 0.05). Prognostic models of BCC combined with CPF had the highest predictive performance, and the area under the curve (AUC) for OS and PFS were 0.837, 0.846, respectively. The concordance index (C-index) of nomograms for OS and PFS were 0.834, 0.799, respectively. Calibration curves showed good agreement between the nomograms’ predictive and actual OS and PFS, decision curve analysis (DCA) showed good clinical benefit of nomograms. CONCLUSION: CT-based body composition changes and CPF (SCC-Ag, LNM) were associated with survival in patients with LACC. The prognostic nomograms combined BCC with CPF were able to predict the OS and PFS in patients with LACC reliably. Show more

Keywords: Locally advanced cervical cancer, quantitative computer tomography-based body composition, survival

DOI: 10.3233/XST-230212

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 427-441, 2024

Authors: Moock, Verena M. | Arce Chávez, Darien E. | García-Segundo, Crescencio | Ruiz-Huerta, Leopoldo

Article Type: Research Article

Abstract: BACKGROUND: The environmental impact on industrial X-ray tomography systems has gained its attention in terms of image precision and metrology over recent years, yet is still complex due to the variety of applications. OBJECTIVE: The current study explores the photothermal repercussions of the overall radiation exposure time. It shows the emerging dimensional uncertainty when measuring a stainless steel sphere by means of circular tomography scans. METHODS: The authors develop a novel frame difference method for X-ray radiographies to evaluate the spatial changes induced in the projected absorption maps on the X-ray panel. The …object of interest has a simple geometry for the purpose of proof of concept. The dominant source of the observed radial uncertainty is the photothermal effect due to high-energy X-ray scattering at the metal workpiece. Thermal variations are monitored by an infrared camera within the industrial tomography system, which confines that heat in the industrial grade X-ray system. RESULTS: The authors demonstrate that dense industrial computed tomography programs with major X-ray power notably affect the uncertainty of digital dimensional measurements. The registered temperature variations are consistent with dimensional changes in radiographies and hence form a source of error that might result in visible artifacts within the 3D image reconstruction. CONCLUSIONS: This contribution is of fundamental value to reach the balance between the number of projections and radial uncertainty tolerance when performing analysis with X-ray dimensional exploration in precision measurements with industrial tomography. Show more

Keywords: computed tomography, digital manufacturing, digital metrology, infrared imaging, thermal expansion, photothermal effect

DOI: 10.3233/XST-230260

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 443-458, 2024

Article Type: Other

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 459-459, 2024

Authors: Zong, Fangke | Yang, Jun | Jiang, Jun | Guo, JinChuan

Article Type: Research Article

Abstract: 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. Show more

Keywords: X-ray optics, phase-contrast, X-ray imaging, moire artifacts, grating

DOI: 10.3233/XST-230202

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 461-473, 2024

Authors: Alshamrani, Khalaf | Alshamrani, Hassan A.

Article Type: Research Article

Abstract: BACKGROUND: Digital X-ray imaging is essential for diagnosing osteoporosis, but distinguishing affected patients from healthy individuals using these images remains challenging. OBJECTIVE: This study introduces a novel method using deep learning to improve osteoporosis diagnosis from bone X-ray images. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. Show more

Keywords: Lossless compression, classification, Bone Xray, ROI, patch size, osteoporosis

DOI: 10.3233/XST-230238

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 2, pp. 475-491, 2024