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