Journal of X-Ray Science and Technology - Volume Pre-press, issue Pre-press
Purchase individual online access for 1 year to this journal.
Price: EUR 160.00
Impact Factor 2019: 1.662
Journal of X-Ray Science and Technology is an international journal designed for the diverse community (biomedical, industrial and academic) of users and developers of novel x-ray imaging techniques. The purpose of the journal is to provide clear and full coverage of new developments and applications in the field.
Areas such as x-ray microlithography, x-ray astronomy and medical x-ray imaging as well as new technologies arising from fields traditionally considered unrelated to x rays (semiconductor processing, accelerator technology, ionizing and non-ionizing medical diagnostic and therapeutic modalities, etc.) present opportunities for research that can meet new challenges as they arise.
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.
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.
Keywords: COVID-19, Chest X-Ray Image, transfer learning, image identification
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.
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.
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.
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.
Keywords: 3-D medical image, multi-modal image fusion, directional structure tensor, local entropy, detail component
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.
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.
Keywords: X-ray, angular distribution, particle induced X-ray emission (PIXE), electron impact, alignment of atoms
Abstract: OBJECTIVE: Diagnosis of tuberculosis (TB) on 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 the consecutive existence of 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, 0.971, 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.
Keywords: Detection of Active tuberculosis (ATB), classification between ATB and non-ATB
cases, artificial intelligence (AI), U-Net based deep learning
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…analysis 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.
Keywords: Osteoporosis, bone mineral density (BMD), dual-energy X-ray absorptiometry (DEXA), deep learning, attention unit, U-net, Dice value, and T-Score