Journal of X-Ray Science and Technology - Volume 28, issue 5

Authors: Rahaman, Md Mamunur | Li, Chen | Yao, Yudong | Kulwa, Frank | Rahman, Mohammad Asadur | Wang, Qian | Qi, Shouliang | Kong, Fanjie | Zhu, Xuemin | Zhao, Xin

Article Type: Research Article

Abstract: BACKGROUND: The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system. OBJECTIVE: One of the critical factors behind the rapid spread of COVID-19 pandemic is a lengthy clinical testing time. The imaging tool, such as Chest X-ray (CXR), can speed up the identification process. Therefore, our objective is to develop an …automated CAD system for the detection of COVID-19 samples from healthy and pneumonia cases using CXR images. METHODS: Due to the scarcity of the COVID-19 benchmark dataset, we have employed deep transfer learning techniques, where we examined 15 different pre-trained CNN models to find the most suitable one for this task. RESULTS: A total of 860 images (260 COVID-19 cases, 300 healthy and 300 pneumonia cases) have been employed to investigate the performance of the proposed algorithm, where 70% images of each class are accepted for training, 15% is used for validation, and rest is for testing. It is observed that the VGG19 obtains the highest classification accuracy of 89.3% with an average precision, recall, and F1 score of 0.90, 0.89, 0.90, respectively. CONCLUSION: This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images. Show more

Keywords: COVID-19, Chest X-Ray Image, transfer learning, image identification

DOI: 10.3233/XST-200715

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 5, pp. 821-839, 2020

Authors: Albahli, Saleh | Albattah, Waleed

Article Type: Research Article

Abstract: OBJECTIVE: This study aims to employ the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of novel coronavirus (COVID-19) infected disease. METHOD: This study applied transfer learning method to develop deep learning models for detecting COVID-19 disease. Three existing state-of-the-art deep learning models namely, Inception ResNetV2, InceptionNetV3 and NASNetLarge, were selected and fine-tuned to automatically detect and diagnose COVID-19 disease using chest X-ray images. A dataset involving 850 images with the confirmed COVID-19 disease, 500 images of community-acquired (non-COVID-19) pneumonia cases and 915 normal chest …X-ray images was used in this study. RESULTS: Among the three models, InceptionNetV3 yielded the best performance with accuracy levels of 98.63% and 99.02% with and without using data augmentation in model training, respectively. All the performed networks tend to overfitting (with high training accuracy) when data augmentation is not used, this is due to the limited amount of image data used for training and validation. CONCLUSION: This study demonstrated that a deep transfer learning is feasible to detect COVID-19 disease automatically from chest X-ray by training the learning model with chest X-ray images mixed with COVID-19 patients, other pneumonia affected patients and people with healthy lungs, which may help doctors more effectively make their clinical decisions. The study also gives an insight to how transfer learning was used to automatically detect the COVID-19 disease. In future studies, as the amount of available dataset increases, different convolution neutral network models could be designed to achieve the goal more efficiently. Show more

Keywords: Deep learning, novel coronavirus, detection of COVID-19, chest X-ray images, automatic detections, transfer learning, InceptioNetV3, NASNetlarge

DOI: 10.3233/XST-200720

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 5, pp. 841-850, 2020

Authors: Zhang, Hua | Liu, Xiaohong | Yu, Peng | Cheng, Mingyuan | Wang, Weiting | Sun, Yipeng | Zeng, Bingliang | Fan, Bing

Article Type: Research Article

Abstract: OBJECTIVES: To assess prognosis or dynamic change from initial diagnosis until recovery of the patients with moderate coronavirus disease (COVID-19) pneumonia using chest CT images. MATERIALS AND METHODS: In this retrospective study, 33 patients (18 men, 15 women; median age, 49.0 years) with confirmed with moderate COVID-19 pneumonia in a multicenter hospital were included. The patients underwent at least four chest non-contrast-enhanced computed tomography (CT) scans at approximately 5-day intervals. We analyzed the clinical and CT characteristics of the patients. Moreover, the total CT score and the sum of lung involvement were determined for every CT scan. …RESULTS: The most widespread presenting symptoms were fever (32/33, 97.0%) and cough (17/33, 51.5%), which were often accompanied by decreased lymphocyte count (15/33, 45.5%) and increased C-reactive protein levels (18/33, 54.6%). Bilateral, multifocal ground glass opacities (32/33, 97.0%), consolidation (25/33, 75.8%), vascular thickening (23/33, 69.7%), and bronchial wall thickening (21/33, 63.6%) with peripheral distribution were the most frequent CT findings during moderate COVID-19 pneumonia. In patients recovering from moderate COVID-19 pneumonia, four stages (stages 1–4) of evolution were identified on chest CT with average CT scores of 3.4±2.3, 6.0±4.4, 5.6±3.8, and 4.9±3.2, respectively, from the onset of symptoms. For most patients, the peak of average total CT score increased for approximately 8 days after the onset of symptoms, after which it decreased gradually. The mean CT score of all patients was 4.7 at the time of discharge. CONCLUSION: The moderate COVID-19 pneumonia CT score increased rapidly in a short period of time initially, followed by a slow decline over a relatively long time. The peak of the course occurred in stage 2. Complete recovery of patients with moderate COVID-19 pneumonia with high mean CT score at the time of discharge requires longer time. Show more

Keywords: Coronavirus disease, COVID-19, Moderate COVID-19 Pneumonia, COVID-19 prognosis, Dynamic CT assessment

DOI: 10.3233/XST-200711

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 5, pp. 851-861, 2020

Authors: Shen, Cong | Yu, Nan | Cai, Shubo | Zhou, Jie | Sheng, Jiexin | Liu, Kang | Zhou, Heping | Guo, Youmin

Article Type: Research Article

Abstract: OBJECTIVES: This study aims to trace the dynamic lung changes of coronavirus disease 2019 (COVID-19) using computed tomography (CT) images by a quantitative method. METHODS: In this retrospective study, 28 confirmed COVID-19 cases with 145 CT scans are collected. The lesions are detected automatically and the parameters including lesion volume (LeV/mL), lesion percentage to lung volume (LeV%), mean lesion density (MLeD/HU), low attenuation area lower than – 400HU (LAA-400%), and lesion weight (LM/mL*HU) are computed for quantification. The dynamic changes of lungs are traced from the day of initial symptoms to the day of discharge. The lesion distribution …among the five lobes and the dynamic changes in each lobe are also analyzed. RESULTS: LeV%, MLeD, and LM reach peaks on days 9, 6 and 8, followed by a decrease trend in the next two weeks. LAA-400% (mostly the ground glass opacity) declines to the lowest on days 4–5, and then increases. The lesion is mostly seen in the bilateral lower lobes, followed by the left upper lobe, right upper lobe and right middle lobe (p  < 0.05). The right middle lobe is the earliest one (on days 6–7), while the right lower lobe is the latest one (on days 9–10) that reaches to peak among the five lobes. CONCLUSIONS: Severity of COVID-19 increases from the day of initial symptoms, reaches to the peak around on day 8, and then decreases. Lesion is more commonly seen in the bilateral lower lobes. Show more

Keywords: Coronavirus disease 2019 (COVID-19), Pneumonia, analysis of CT images, computer-assisted quantification, evaluation study

DOI: 10.3233/XST-200721

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 5, pp. 863-873, 2020

Authors: Gu, Qianbiao | Ouyang, Xin | Xie, An | Tan, Xianzheng | Liu, Jianbin | Huang, Feng | Liu, Peng

Article Type: Research Article

Abstract: OBJECTIVE: To retrospectively analyze and stratify the initial clinical features and chest CT imaging findings of patients with COVID-19 by gender and age. METHODS: Data of 50 COVID-19 patients were collected in two hospitals. The clinical manifestations, laboratory examination and chest CT imaging features were analyzed, and a stratification analysis was performed according to gender and age [younger group: <50 years old, elderly group ≥50 years old]. RESULTS: Most patients had a history of epidemic exposure within 2 weeks (96%). The main clinical complaints are fever (54%) and cough (46%). In chest CT images, ground-glass opacity …(GGO) is the most common feature (37/38, 97%) in abnormal CT findings, with the remaining 12 patients (12/50, 24%) presenting normal CT images. Other concomitant abnormalities include dilatation of vessels in lesion (76%), interlobular thickening (47%), adjacent pleural thickening (37%), focal consolidation (26%), nodules (16%) and honeycomb pattern (13%). The lesions were distributed in the periphery (50%) or mixed (50%). Subgroup analysis showed that there was no difference in the gender distribution of all the clinical and imaging features. Laboratory findings, interlobular thickening, honeycomb pattern and nodules demonstrated remarkable difference between younger group and elderly group. The average CT score for pulmonary involvement degree was 5.0±4.7. Correlation analysis revealed that CT score was significantly correlated with age, body temperature and days from illness onset (p  < 0.05). CONCLUSIONS: COVID-19 has various clinical and imaging appearances. However, it has certain characteristics that can be stratified. CT plays an important role in disease diagnosis and early intervention. Show more

Keywords: COVID-19, SARS-CoV-2, novel coronavirus-infected pneumonia, X-ray computed tomography, analysis of CT imaging findings

DOI: 10.3233/XST-200709

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 5, pp. 875-884, 2020

Authors: Su, Ying | Han, Yi | Liu, Jie | Qiu, Yue | Tan, Qian | Zhou, Zhen | Yu, Yi-zhou | Chen, Jun | Giger, Maryellen L. | Lure, Fleming Y. M. | Luo, Zhe

Article Type: Research Article

Abstract: In this article, we analyze and report cases of three patients who were admitted to Renmin Hospital, Wuhan University, China, for treating COVID-19 pneumonia in February 2020 and were unresponsive to initial treatment of steroids. They were then received titrated steroids treatment based on the assessment of computed tomography (CT) images augmented and analyzed with the artificial intelligence (AI) tool and output. Three patients were finally recovered and discharged. The result indicated that sufficient steroids may be effective in treating the COVID-19 patients after frequent evaluation and timely adjustment according to the disease severity assessed based on the quantitative analysis …of the images of serial CT scans. Show more

Keywords: Coronavirus, pneumonia, treating COVID-19 patients, steroids, computerized tomography, image analysis using artificial intelligence

DOI: 10.3233/XST-200710

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 5, pp. 885-892, 2020

Authors: Harun, H.H. | Karim, M.K.A. | Abbas, Z. | Sabarudin, A. | Muniandy, S.C. | Ibahim, M.J.

Article Type: Research Article

Abstract: PURPOSE: To evaluate the influence of iterative reconstruction (IR) levels on Computed Tomography (CT) image quality and to establish Figure of Merit (FOM) value for CT Pulmonary Angiography (CTPA) examinations. METHODS: Images of 31 adult patients who underwent CTPA examinations in our institution from March to April 2019 were retrospectively collected. Other data, such as scanning parameters, radiation dose and body habitus information from the subjects were also recorded. Six different levels of IR were applied to the volume data of the subjects. Five circles of the region of interest (ROI) were drawn in five different arteries namely, …pulmonary trunk, right pulmonary artery, left pulmonary artery, ascending aorta and descending aorta. The mean Signal-to-noise ratio (SNR) was obtained, and the FOM was calculated in a fraction of the SNR2 divided by volume-weighted CT dose index (CTDIvol ) and SNR2 divided by the size-specific dose estimates (SSDE). RESULTS: Overall, we observed that the mean value of CTDIvol and SSDE were 13.79±7.72 mGy and 17.25±8.92 mGy, respectively. Notably, SNR values significantly increase with increase of the IR level (p  < 0.05). There are also significant differences (p  < 0.05) in the FOM for both SNR2 /SSDE and SNR2 /CTDIvol attained in different IR levels. CONCLUSION: We successfully evaluate the value of radiation dose and image quality performance and set up a figure of merit for both parameters to further verify scanning protocols by radiology personnel. Show more

Keywords: Signal-to-noise ratio, figure-of-merit, CT pulmonary angiography, pulmonary embolism, iterative reconstruction algorithm

DOI: 10.3233/XST-200699

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 5, pp. 893-903, 2020

Authors: Li, Qingqing | Chen, Ke | Han, Lin | Zhuang, Yan | Li, Jingtao | Lin, Jiangli

Article Type: Research Article

Abstract: BACKGROUND: Automatic segmentation of individual tooth root is a key technology for the reconstruction of the three-dimensional dental model from Cone Beam Computed Tomography (CBCT) images, which is of great significance for the orthodontic, implant and other dental diagnosis and treatment planning. OBJECTIVES: Currently, tooth root segmentation is mainly done manually because of the similar gray of the tooth root and the alveolar bone from CBCT images. This study aims to explore the automatic tooth root segmentation algorithm of CBCT axial image sequence based on deep learning. METHODS: We proposed a new automatic tooth root segmentation …method based on the deep learning U-net with AGs. Since CBCT sequence has a strong correlation between adjacent slices, a Recurrent neural network (RNN) was applied to extract the intra-slice and inter-slice contexts. To develop and test this new method for automatic segmentation of tooth roots using CBCT images, 24 sets of CBCT sequences containing 1160 images and 5 sets of CBCT sequences containing 361 images were used to train and test the network, respectively. RESULTS: Applying to the testing dataset, the segmentation accuracy measured by the intersection over union (IOU), dice similarity coefficient (DICE), average precision rate (APR), average recall rate (ARR), and average symmetrical surface distance (ASSD) are 0.914, 0.955, 95.8% , 95.3% , 0.145 mm, respectively. CONCLUSIONS: The study demonstrates that the new method combining attention U-net with RNN yields the promising results of automatic tooth roots segmentation, which has potential to help improve the segmentation efficiency and accuracy in future clinical practice. Show more

Keywords: Tooth roots, automatic segmentation, cone beam computed tomography, attention U-net, RNN

DOI: 10.3233/XST-200678

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 5, pp. 905-922, 2020

Authors: Ben Slama, Amine | Sahli, Hanene | Mouelhi, Aymen | Marrakchi, Jihene | Boukriba, Seif | Trabelsi, Hedi | Sayadi, Mounir

Article Type: Research Article

Abstract: BACKGROUD AND OBJECTIVE: The control of clinical manifestation of vestibular system relies on an optimal diagnosis. This study aims to develop and test a new automated diagnostic scheme for vestibular disorder recognition. METHODS: In this study we stratify the Ellipse-fitting technique using the Video Nysta Gmographic (VNG) sequence to obtain the segmented pupil region. Furthermore, the proposed methodology enabled us to select the most optimum VNG features to effectively conduct quantitative evaluation of nystagmus signal. The proposed scheme using a multilayer neural network classifier (MNN) was tested using a dataset involving 98 patients affected by VD and 41 …normal subjects. RESULTS: The new MNN scheme uses only five temporal and frequency parameters selected out of initial thirteen parameters. The scheme generated results reached 94% of classification accuracy. CONCLUSIONS: The developed expert system is promising in solving the problem of VNG analysis and achieving accurate results of vestibular disorder recognition or diagnosis comparing to other methods or classifiers. Show more

Keywords: VNG system, pupil tracking, nystagmus measurement, multilayer neural network (MNN), convolutional neural network (CNN)

DOI: 10.3233/XST-200661

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 5, pp. 923-938, 2020

Authors: Ma, Luyao | Wang, Yun | Guo, Lin | Zhang, Yu | Wang, Ping | Pei, Xu | Qian, Lingjun | Jaeger, Stefan | Ke, Xiaowen | Yin, Xiaoping | Lure, Fleming Y.M.

Article Type: Research Article

Abstract: OBJECTIVE: Diagnosis of tuberculosis (TB) in multi-slice spiral computed tomography (CT) images is a difficult task in many TB prevalent locations in which experienced radiologists are lacking. To address this difficulty, we develop an automated detection system based on artificial intelligence (AI) in this study to simplify the diagnostic process of active tuberculosis (ATB) and improve the diagnostic accuracy using CT images. DATA: A CT image dataset of 846 patients is retrospectively collected from a large teaching hospital. The gold standard for ATB patients is sputum smear, and the gold standard for normal and pneumonia patients is the …CT report result. The dataset is divided into independent training and testing data subsets. The training data contains 337 ATB, 110 pneumonia, and 120 normal cases, while the testing data contains 139 ATB, 40 pneumonia, and 100 normal cases, respectively. METHODS: A U-Net deep learning algorithm was applied for automatic detection and segmentation of ATB lesions. Image processing methods are then applied to CT layers diagnosed as ATB lesions by U-Net, which can detect potentially misdiagnosed layers, and can turn 2D ATB lesions into 3D lesions based on consecutive U-Net annotations. Finally, independent test data is used to evaluate the performance of the developed AI tool. RESULTS: For an independent test, the AI tool yields an AUC value of 0.980. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value are 0.968, 0.964, 0.971, 0.971, and 0.964, respectively, which shows that the AI tool performs well for detection of ATB and differential diagnosis of non-ATB (i.e. pneumonia and normal cases). CONCLUSION: An AI tool for automatic detection of ATB in chest CT is successfully developed in this study. The AI tool can accurately detect ATB patients, and distinguish between ATB and non- ATB cases, which simplifies the diagnosis process and lays a solid foundation for the next step of AI in CT diagnosis of ATB in clinical application. Show more

Keywords: Active tuberculosis (ATB), artificial intelligence (AI), deep learning

DOI: 10.3233/XST-200662

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 5, pp. 939-951, 2020

Authors: Nazia Fathima, S.M. | Tamilselvi, R. | Parisa Beham, M. | Sabarinathan, D.

Article Type: Research Article

Abstract: BACKGROUND: Osteoporosis, a silent killing disease of fracture risk, is normally determined based on the bone mineral density (BMD) and T-score values measured in bone. However, development of standard algorithms for accurate segmentation and BMD measurement from X-ray images is a challenge in the medical field. OBJECTIVE: The purpose of this work is to more accurately measure BMD from X-ray images, which can overcome the limitations of the current standard technique to measure BMD using Dual Energy X-ray Absorptiometry (DEXA) such as non-availability and inaccessibility of DEXA machines in developing countries. In addition, this work also attempts to …analyze the DEXA scan images for better segmentation and measurement of BMD. METHODS: This work employs a modified U-Net with Attention unit for accurate segmentation of bone region from X-Ray and DEXA images. A linear regression model is developed to compute BMD and T-score. Based on the value of T-score, the images are then classified as normal, osteopenia or osteoporosis. RESULTS: The proposed network is experimented with the two internally collected datasets namely, DEXSIT and XSITRAY, comprised of DEXA and X-ray images, respectively. The proposed method achieved an accuracy of 88% on both datasets. The Dice score on DEXSIT and XSITRAY is 0.94 and 0.92, respectively. CONCLUSION: Our modified U-Net with attention unit achieves significantly higher results in terms of Dice score and classification accuracy. The computed BMD and T-score values of the proposed method are also compared with the respective clinical reports for validation. Hence, using the digitized X-Ray images can be used to detect osteoporosis efficiently and accurately. Show more

Keywords: Osteoporosis, bone mineral density (BMD), dual-energy X-ray absorptiometry (DEXA), deep learning, attention unit, U-net, Dice value, and T-Score

DOI: 10.3233/XST-200692

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 5, pp. 953-973, 2020

Authors: Vellakani, Sivamurugan | Pushbam, Indumathi

Article Type: Research Article

Abstract: Human eye is affected by the different eye diseases including choroidal neovascularization (CNV), diabetic macular edema (DME) and age-related macular degeneration (AMD). This work aims to design an artificial intelligence (AI) based clinical decision support system for eye disease detection and classification to assist the ophthalmologists more effectively detecting and classifying CNV, DME and drusen by using the Optical Coherence Tomography (OCT) images depicting different tissues. The methodology used for designing this system involves different deep learning convolutional neural network (CNN) models and long short-term memory networks (LSTM). The best image captioning model is selected after performance analysis by comparing …nine different image captioning systems for assisting ophthalmologists to detect and classify eye diseases. The quantitative data analysis results obtained for the image captioning models designed using DenseNet201 with LSTM have superior performance in terms of overall accuracy of 0.969, positive predictive value of 0.972 and true-positive rate of 0.969using OCT images enhanced by the generative adversarial network (GAN). The corresponding performance values for the Xception with LSTM image captioning models are 0.969, 0.969 and 0.938, respectively. Thus, these two models yield superior performance and have potential to assist ophthalmologists in making optimal diagnostic decision. Show more

Keywords: Age-related macular degeneration (AMD), connective tissue, choroidal neovascularization (CNV), light sensitive tissue, Optical Coherence Tomography (OCT), deep learning, convolution neural network (CNN), long short term memory (LSTM), neovascular tissue, surrounding tissue

DOI: 10.3233/XST-200697

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 5, pp. 975-988, 2020

Authors: Lu, Nan-Han | Liu, Yi-Shan | Liu, Ko-In | Hsu, Shih-Yen | Huang, Yung-Hui | Sun, Cheuk-Kwan | Chen, Tai-Been

Article Type: Research Article

Abstract: OBJECTIVE: This study aims to analyze and compare the diagnostic effectiveness of 320-row multi-detector computed tomography for coronary artery angiography (MDCTA) in subjects with and without sublingual vasodilator (nitroglycerin). MATERIALS AND METHODS: From September 2015 to September 2016, 70 individuals without history of major cardiovascular diseases who underwent MDCTA for health examination were retrospectively categorized into sublingual nitroglycerin (NTG) and non-NTG groups. Medical history, CT dose index (CTDI), and multi-slice CT images were compared between two groups. A diameter of coronary artery (DA, mm) was computed and analyzed. RESULTS: A total of 41 males and 29 …females (mean age: 55.43±8.84 years, range: 34– 76) were reviewed. Normal and abnormal MDCTA findings were noted in 54 and 16 participants, respectively, with the detection rate of coronary artery disease being 23%. There was no significant difference in inter-observer variability of coronary CTA image quality and diagnosis between the NTG and non-NTG groups among three experienced radiologists. Although the percentage dilatation of left anterior descending branch (LAD), right coronary artery (RCA) and left circumflex branch (LCX) following in the NTG group were 12.4%, 12.8% and 25.3%, respectively (p  < 0.01), there was no significant difference in image quality and diagnosis between the two groups. CONCLUSIONS: Despite the recommendation of routine nitroglycerin use for subjects undergoing computed tomography for coronary artery angiography, our results showed no significant advantage of its use in improving image quality and rate of diagnosis accuracy. Show more

Keywords: Multi-detector computed tomography for coronary artery angiography (MDCTA), coronary artery disease, nitroglycerin

DOI: 10.3233/XST-200652

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 5, pp. 989-999, 2020

Authors: Wang, Yu | Wang, Yuanjun

Article Type: Research Article

Abstract: BACKGROUND: Multi-modal medical image fusion plays a crucial role in many areas of modern medicine like diagnosis and therapy planning. OBJECTIVE: Due to the factor that the structure tensor has the property of preserving the image geometry, we utilized it to construct the directional structure tensor and further proposed an improved 3-D medical image fusion method. METHOD: The local entropy metrics were used to construct the gradient weights of different source images, and the eigenvectors of traditional structure tensor were combined with the second-order derivatives of image to construct the directional structure tensor. In addition, the …guided filtering was employed to obtain detail components of the source images and construct a fused gradient field with the enhanced detail. Finally, the fusion image was generated by solving the functional minimization problem. RESULTS AND CONCLUSION: Experimental results demonstrated that this new method is superior to the traditional structure tensor and multi-scale analysis in both visual effect and quantitative assessment. Show more

Keywords: 3-D medical image, multi-modal image fusion, directional structure tensor, local entropy, detail component

DOI: 10.3233/XST-200684

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 5, pp. 1001-1016, 2020

Authors: Hu, Pengfei | Wang, Xing

Article Type: Research Article

Abstract: It is of great importance to study the alignment of atoms in collision process in elementary analysis with a Particle Induced X-ray Emission (PIXE) technique. The measurement of alignment can also offer an effective testing ground for developing theory models in ionization process. The typical L X-ray spectra are measured for Ag thin target by 15 keV electron impact at emission angles from 0° to 25°. Angular dependence of intensity ratios Lα /Lβ 1 , Lβ 2 /Lβ 1 and Lγ /Lβ 1 are investigated as a function of the second-order Legendre polynomial P 2 (cosθ ). …This study found that Lβ 2 line exhibits anisotropic emission spatially, while the emission of Lα , Lβ 1 and Lγ 1 lines is isotropic. The results are interpreted by the influence of the Coster-Kronig (CK) transitions on the spatial distribution of X-ray emission. The anisotropy parameter β for Lβ 2 lines is obtained experimentally and consequently the alignment degree A 20 for L3 subshell is determined by taking CK transition into account. Namely, the alignment does exist in L3 -subshell for atomic ionization by electron impact. The measurements offer an evidence to the existence of alignment for atomic ionization in electron-impact process. Show more

Keywords: X-ray, angular distribution, particle induced X-ray emission (PIXE), electron impact, alignment of atoms

DOI: 10.3233/XST-200701

Citation: Journal of X-Ray Science and Technology, vol. 28, no. 5, pp. 1017-1023, 2020