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DEBISim: A simulation pipeline for dual energy CT-based baggage inspection systems J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2021-02-24 Ankit Manerikar; Fangda Li; Avinash C. Kak
BACKGROUND:Materials characterization made possible by dual energy CT (DECT) scanners is expected to considerably improve automatic detection of hazardous objects in checked and carry-on luggage at our airports. Training a computer to identify the hazardous items from DECT scans however implies training on a baggage dataset that can represent all the possible ways a threat item can packed inside a
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A feasibility study of realizing low-dose abdominal CT using deep learning image reconstruction algorithm J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2021-02-18 Lu-Lu Li; Huang Wang; Jian Song; Jin Shang; Xiao-Ying Zhao; Bin Liu
OBJECTIVES:To explore the feasibility of achieving diagnostic images in low-dose abdominal CT using a Deep Learning Image Reconstruction (DLIR) algorithm. METHODS:Prospectively enrolled 47 patients requiring contrast-enhanced abdominal CT scans. The late-arterial phase scan was added and acquired using lower-dose mode (tube current range, 175–545 mA; 80 kVp for patients with BMI ≤24 kg/m2 and 100 kVp
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Screening of COVID-19 based on the extracted radiomics features from chest CT images J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2021-02-17 Seyed Masoud Rezaeijo; Razzagh Abedi-Firouzjah; Mohammadreza Ghorvei; Samad Sarnameh
Abstract BACKGROUND AND OBJECTIVE: Radiomics has been widely used in quantitative analysis of medical images for disease diagnosis and prognosis assessment. The objective of this study is to test a machine-learning (ML) method based on radiomics features extracted from chest CT images for screening COVID-19 cases. METHODS: The study is carried out on two groups of patients, including 138 patients with
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Optically transparent glass modified with metal oxides for X-rays and gamma rays shielding material J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2021-02-11 Khalid I. Hussein; Mohammed S. Alqahtani; Iwona Grelowska; Manuela Reben; Hesham Afif; Heba Zahran; I. S. Yaha; El Sayed Yousef
BACKGROUND:Metal oxide glass composites have attracted huge interest as promising shielding materials to replace toxic, heavy, and costly conventional shielding materials. OBJECTIVE:In this work, we evaluate shielding effectiveness of four novel tellurite-based glasses samples doped with oxide metals (namely, A, B, C, and D, which are 75TeO2- 10P2O5- 10ZnO- 5PbF2- 0.24Er2O3 ; 70TeO2- 10P2O5- 10ZnO-
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Development of a novel computational method using computed tomography images for the early detection and severity classification of COVID-19 cases J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2021-02-05 M.A. Abbas; M.S. Alqahtani; A.J. Alkulib; H.M. Almohiy; R.F. Alshehri; E.A. Alamri; A.A. Alamri
Abstract BACKGROUND: Recent occurrence of the 2019 coronavirus disease (COVID-19) outbreak, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has highlighted the need for fast, accurate, and simple strategies to identify cases on a large scale. OBJECTIVE: This study aims to develop and test an accurate detection and severity classification methodology that may help medical
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X- ray absorption parameters studies of P 2O 5- SnCl 2-SnO bioactive glass system J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2021-01-30 Abdullah M.S. Alhuthali; Ashok Kumar; M.I. Sayyed; Y. Al-Hadeethi
The main objective of this work is to explore the X-ray interaction properties of P2O5- SnCl2-SnO bioactive glass system using Photon Shielding and Dosimetry (Phys-X/PSD) software in the energy range 10–150 keV. The study of these parameters will have applications in various fields of nuclear medicine, medical technology, and other medical applications. The value of mass attenuation coefficients (μm)
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Impact of respiratory motion artifact on coronary image quality of one beat coronary CT angiography J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2021-01-30 Wenting Shen; Yang Chen; Wen Qian; Wangyan Liu; Yinsu Zhu; Yi Xu; Xiaomei Zhu
BACKGROUND:Accuracy of CT-derived fractional flow reserve depends on good image quality. Thus, improving image quality during coronary CT angiography (CCTA) is important. OBJECTIVE:To investigate impact of respiratory motion artifact on coronary image quality focusing on vessel diameter and territory during one beat CCTA by a 256-row detector. METHODS:We retrospectively reviewed patients who underwent
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Quality improvement of general anteroposterior radiographic image of vertebral body according to optimum angle of incidence J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2021-01-30 Jong Hyeok Kwak; Chi Hyung Lee; Gyeong Rip Kim; Sang Weon Lee; Young Ha Kim; Geun Sung Song; Dong Wuk Son; Hynu Chul Sung; Jin Sung Kwak; Soon Ki Sung
OBJECTIVE:In this study, we present an appropriate angle of incidence to reduce the distortions in images of L4 and L5 during a general anteroposterior radiograph examination. METHOD:We selected 170 patients who had normal radiological findings among those who underwent anteroposterior and lateralexamination for lumbar vertebrae. An optimum angle of incidence wa suggested through the statistical analysis
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Predictive role of T2WI and ADC-derived texture parameters in differentiating Gleason score 3 + 4 and 4 + 3 prostate cancer J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2021-01-23 Zhen Kang; Anhui Xu; Liang Wang
BACKGROUND:Since Gleason score (GS) 4 + 3 prostate cancer (PCa) has the worse prognosis than GS 3 + 4 PCa, differentiating these two types of PCa is of clinical significance. OBJECTIVE:To assess the predictive roles of using T2WI and ADC-derived image texture parameters in differentiating GS 3 + 4from GS 4 + 3 PCa. METHODS:Forty-eight PCa patients of GS 3 + 4 and 37 patients of GS 4 + 3 are retrieved
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Calculation of effective atomic numbers using a rational polynomial approximation method with a dual-energy X-ray imaging system J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2021-01-21 Chia-Hao Chang; Yu-Ching Ni; Sheng-Pin Tseng
The study aims to develop a rational polynomial approximation method for improving the accuracy of the effective atomic number calculation with a dual-energy X-ray imaging system. This method is based on a multi-materials calibration model with iterative optimization, which can improve the calculation accuracy of the effective atomic number by adding a rational term without increasing the computation
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Coronary microvascular dysfunction: An important interpretation on the clinical significance of transient ischemic dilation of the left ventricle on myocardial perfusion imaging J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2021-01-17 Liang Chen; Min Zhang; Jinqi Jiang; Bei Lei; Xiaoyan Sun
PURPOSE:To further investigate the clinical significance of transient ischemic dilation (TID) on myocardial perfusion imaging (MPI) by analyzing the effect of anisodamine hydrobromide (a drug that can effectively ameliorate microcirculation) on the patients with isolated TID and the findings of previous literatures. METHODS:Total 107 patients with isolated TID (TID value≥1.11) were randomly divided
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Computer aid screening of COVID-19 using X-ray and CT scan images: An inner comparison J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2021-01-17 Prabira Kumar Sethy; Santi Kumari Behera; Komma Anitha; Chanki Pandey; M.R. Khan
Abstract The objective of this study is to conduct a critical analysis to investigate and compare a group of computer aid screening methods of COVID-19 using chest X-ray images and computed tomography (CT) images. The computer aid screening method includes deep feature extraction, transfer learning, and machine learning image classification approach. The deep feature extraction and transfer learning
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A metal artifact reduction scheme in CT by a Poisson fusion sinogram based postprocessing method J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2021-01-12 Hui Tang; Yu Bing Lin; Guo Yan Sun; Xu Dong Bao
OBJECTIVE:To reduce metal artifacts generated using current interpolation-based method on X-ray computed tomography (CT) images, this study proposes and tests a new Poisson fusion sinogram based metal artifact reduction (FS-MAR) method. METHODS:The proposed FS-MAR method consists of (1) generatingthe prior image, (2) forward projecting this prior image and applying the Poisson blending technique to
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COVID-19 diagnosis from chest X-ray images using transfer learning: Enhanced performance by debiasing dataloader J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2021-01-11 Çağín Polat; Onur Karaman; Ceren Karaman; Güney Korkmaz; Mehmet Can Balcí; Sevim Ercan Kelek
Abstract BACKGROUND: Chest X-ray imaging has been proved as a powerful diagnostic method to detect and diagnose COVID-19 cases due to its easy accessibility, lower cost and rapid imaging time. OBJECTIVE: This study aims to improve efficacy of screening COVID-19 infected patients using chest X-ray images with the help of a developed deep convolutional neural network model (CNN) entitled nCoV-NET. METHODS:
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Iatrogenic ureteral injury during retroperitoneal laparoscopy for large renal cysts: What we learned and a review of the literature J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2021-01-08 Zejian Zhang; Dong Chen; Ling Deng; Wei Li; Xisheng Wang; Yixiang Zhang; Fang Liekui; Michael P. Feloney; Yuanyuan Zhang
PURPOSE:To avoid Iatrogenic ureteral injury during retroperitoneal laparoscopy for large renal cyst (diameter > 70 mm), we present two cases of iatrogenic ureteral injury and discuss their clinical courses and final outcomes. PATIENTS AND METHODS:Two male patients (47 years old and 74 years old) with large left simple renal cysts underwent a retroperitoneal laparoscopic operation to treat the cysts
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Dual residual convolutional neural network (DRCNN) for low-dose CT imaging J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2021-01-08 Zhiwei Feng; Ailong Cai; Yizhong Wang; Lei Li; Li Tong; Bin Yan
The excessive radiation doses in the application of computed tomography (CT) technology pose a threat to the health of patients. However, applying a low radiation dose in CT can result in severe artifacts and noise in the captured images, thus affecting the diagnosis. Therefore, in this study, we investigate a dual residual convolution neural network (DRCNN) for low-dose CT (LDCT) imaging, whereby
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Hybrid diffusion tensor imaging feature-based AD classification J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-12-10 Lan Deng; Yuanjun Wang
BACKGROUND:Effective detection of Alzheimer’s disease (AD) is still difficult in clinical practice. Therefore, establishment of AD detection model by means of machine learning is of great significance to assist AD diagnosis. OBJECTIVE:To investigate and test a new detection model aiming to help doctors diagnose AD more accurately. METHODS:Diffusion tensor images and the corresponding T1w images acquired
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A pilot study of radiomics signature based on biparametric MRI for preoperative prediction of extrathyroidal extension in papillary thyroid carcinoma J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-12-10 Junlin He; Heng Zhang; Xian Wang; Zongqiong Sun; Yuxi Ge; Kang Wang; Chunjing Yu; Zhaohong Deng; Jianxin Feng; Xin Xu; Shudong Hu
OBJECTIVE:To investigate efficiency of radiomics signature to preoperatively predict histological features of aggressive extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC) with biparametric magnetic resonance imaging findings. MATERIALS AND METHODS:Sixty PTC patients with preoperative MR including T2WI and T2WI-fat-suppression (T2WI-FS) were retrospectively analyzed. Among them, 35
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Compton-camera-based SPECT for thyroid cancer imaging J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-12-05 Hengyong Yu; Ge Wang
Thyroid cancer is the most common type of endocrine-related cancer and the most common cancer in young women. Currently, single photon emission computed tomography (SPECT) and computed tomography (CT) are used with radioiodine scintigraphy to evaluate patients with thyroid cancer. The gamma camerafor SPECT contains a mechanical collimator that greatly compromises dose efficiency and limits diagnostic
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Accelerated strategy for the MLEM algorithm J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-11-23 Xuan Zheng; Gangrong Qu; Jiajia Zhou
BACKGROUND:A statistical method called maximum likelihood expectation maximization (MLEM) is quite attractive, especially in PET/SPECT. However, the convergence rate of the iterative scheme of MLEM is quite slow. OBJECTIVE:This study aims to develop and test a new method to speed up the convergencerate of the MLEM algorithm. METHODS:We introduce a relaxation parameter in the conventional MLEM iterative
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Comparison of image quality and radiation dose using different pre-ASiR-V and post-ASiR-V levels in coronary computed tomography angiography J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-11-05 Yongxia Zhao; Dongxue Li; Zhichao Liu; Xue Geng; Tianle Zhang; Yize Xu
OBJECTIVE:To determine the optimal pre-adaptive and post-adaptive level statistical iterative reconstruction V (ASiR-V) for improving image quality and reducing radiation dose in coronary computed tomography angiography (CCTA). METHODS:The study was divided into two parts. In part I, 150 patients for CCTA were prospectively enrolled and randomly divided into 5 groups (A, B, C, D, and E) with progressive
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Development of X-ray phase CT with a hybrid configuration of Lau and Talbot–Lau interferometers J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-11-02 Masashi Kageyama; Kenichi Okajima; Minoru Maesawa; Masahiro Nonoguchi; Manabu Nonoguchi; Masaru Kuribayashi; Yukihiro Hara; Atsushi Momose
X-ray phase computed tomography (CT) is used to observe the inside of light materials. In this paper, we report a new study to develop and test a laboratory assembled X-ray phase CT system that comprises an X-ray Lau interferometer, a rotating Mo anode X-ray tube, and a detector with high spatial resolution. The system has a high spatial resolution lower than 10μm, which is evaluated by differentiating
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Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-11-02 Yanhong Yang; Fleming Y.M. Lure; Hengyuan Miao; Ziqi Zhang; Stefan Jaeger; Jinxin Liu; Lin Guo
Abstract Background: Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment. Purpose: In this study, a deep learning algorithm-based AI model using ResUNet network was developed to evaluate the performance of radiologists with and without AI assistance in distinguishing COVID-19 infected pneumonia patients from other pulmonary infections on CT
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Automated location of thyroid nodules in ultrasound images with improved YOLOV3 network J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-10-24 Ling Zhang; Yan Zhuang; Zhan Hua; Lin Han; Cheng Li; Ke Chen; Yulan Peng; Jiangli Lin
BACKGROUND:Thyroid ultrasonography is widely used to diagnose thyroid nodules in clinics. Automatic localization of nodules can promote the development of intelligent thyroid diagnosis and reduce workload of radiologists. However, besides the ultrasound image has low contrast and high noise, the thyroid nodules are diverse in shape and vary greatly in size. Thus, thyroid nodule detection in ultrasound
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An effective sinogram inpainting for complementary limited-angle dual-energy computed tomography imaging using generative adversarial networks J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-10-19 Yizhong Wang; Wenkun Zhang; Ailong Cai; Linyuan Wang; Chao Tang; Zhiwei Feng; Lei Li; Ningning Liang; Bin Yan
Dual-energy computed tomography (DECT) provides more anatomical and functional information for image diagnosis. Presently, the popular DECT imaging systems need to scan at least full angle (i.e., 360°). In this study, we propose a DECT using complementary limited-angle scan (DECT-CL) technology toreduce the radiation dose and compress the spatial distribution of the imaging system. The dual-energy
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Applying a radiomics-based strategy to preoperatively predict lymph node metastasis in the resectable pancreatic ductal adenocarcinoma J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-10-14 Peng Liu; Qianbiao Gu; Xiaoli Hu; Xianzheng Tan; Jianbin Liu; An Xie; Feng Huang
PURPOSE:This retrospective study is designed to develop a Radiomics-based strategy for preoperatively predicting lymph node (LN) status in the resectable pancreatic ductal adenocarcinoma (PDAC) patients. METHODS:Eighty-five patients with histopathological confirmed PDAC are included, of which 35 are LN metastasis positive and 50 are LN metastasis negative. Initially, 1,124 radiomics features are computed
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Predicting fringe visibility in dual-phase grating interferometry with polychromatic x-ray sources J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-10-07 Aimin Yan; Xizeng Wu; Hong Liu
Dual phase grating X-ray interferometry is radiation dose-efficient as compared to common Talbot-Lau grating interferometry. The authors developed a general quantitative theory to predict the fringe visibility in dual-phase grating x-ray interferometry with polychromatic x-ray sources. The derivedformulas are applicable to setups with phase gratings of any phase modulation and with either monochromatic
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Low-dose CT reconstruction method based on prior information of normal-dose image J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-10-02 Zixiang Chen; Qiyang Zhang; Chao Zhou; Mengxi Zhang; Yongfeng Yang; Xin Liu; Hairong Zheng; Dong Liang; Zhanli Hu
BACKGROUND:Radiation risk from computed tomography (CT) is always an issue for patients, especially those in clinical conditions in which repeated CT scanning is required. For patients undergoing repeated CT scanning, a low-dose protocol, such as sparse scanning, is often used, and consequently, anadvanced reconstruction algorithm is also needed. OBJECTIVE:To develop a novel algorithm used for sparse-view
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Material decomposition for simulated dual-energy breast computed tomography via hybrid optimization method J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-10-02 Temitope E. Komolafe; Qiang Du; Yin Zhang; Zhongyi Wu; Cheng Zhang; Ming Li; Jian Zheng; Xiaodong Yang
BACKGROUND:Dual-energy breast CT reconstruction has a potential application that includes separation of microcalcification from healthy breast tissue for assisting early breast cancer detection. OBJECTIVE:To investigate and validate the noise suppression algorithm applied in the decomposition of the simulated breast phantom into microcalcification and healthy breast. METHODS:The proposed hybrid optimization
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Development of a computational tool for estimating computed tomography dose parameters J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-09-19 Hussain M. Almohiy; Khalid I. Hussein; Mohammed S. Alqahtani; Mohammad Rawashdeh; Elhussaien Elshiekh; Madshush M. Alshahrani; Mohammed Saad; Shane Foley; Charbel Saade
BACKGROUND:Computed Tomographic (CT) imaging procedures have been reported as the main source of radiation in diagnostic procedures compared to other modalities. To provide the optimal quality of CT images at the minimum radiation risk to the patient, periodic inspections and calibration tests forCT equipment are required. These tests involve a series of measurements that are time consuming and may
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Automatic tooth roots segmentation of cone beam computed tomography image sequences using U-net and RNN J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-09-19 Qingqing Li; Ke Chen; Lin Han; Yan Zhuang; Jingtao Li; Jiangli Lin
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
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Evaluation of reconstruction algorithms for a stationary digital breast tomosynthesis system using a carbon nanotube X-ray source array. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-09-10 Zhanli Hu,Zixiang Chen,Chao Zhou,Xuda Hong,Jianwei Chen,Qiyang Zhang,Changhui Jiang,Yongshuai Ge,Yongfeng Yang,Xin Liu,Hairong Zheng,Zhicheng Li,Dong Liang
Breast cancer is the most frequently diagnosed cancer in women worldwide. Digital breast tomosynthesis (DBT), which is based on limited-angle tomography, was developed to solve tissue overlapping problems associated with traditional breast mammography. However, due to the problems associated with tube movement during the process of data acquisition, stationary DBT (s-DBT) was developed to allow the
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Fully-automatic segmentation of coronary artery using growing algorithm. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-09-09 Jiali Cui,Hua Guo,Huafeng Wang,Fuqiang Chen,Lixia Shu,Lihong C Li
Currently, cardiac computed tomography angiography (CTA) is widely applied to coronary artery disease diagnosis. Automatic segmentation of coronary artery has played an important role in coronary artery disease diagnosis. In this study, we propose and test a fully automatic coronary artery segmentation method that does not require any human-computer interaction. The proposed method uses a growing strategy
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Deformable registration and region-of-interest image reconstruction in sparse repeat CT scanning. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-09-09 Zeev Adelman,Leo Joskowicz
BACKGROUND:Repeat CT scanning is ubiquitous in many clinical situations, e.g. to follow disease progression, to evaluate treatment efficacy, and to monitor interventional CT procedures. However, it incurs in cumulative radiation to the patient which can be significantly reduced by using a region ofinterest (ROI) and the existing baseline scan. OBJECTIVE:To obtain a high-quality reconstruction of a
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Quick and accurate selection of hand images among radiographs from various body parts using deep learning. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-09-09 Kohei Fujiwara,Fang Wanxuan,Taichi Okino,Kenneth Sutherland,Akira Furusaki,Akira Sagawa,Tamotsu Kamishima
BACKGROUND:Although rheumatoid arthritis (RA) causes destruction of articular cartilage, early treatment significantly improves symptoms and delays progression. It is important to detect subtle damage for an early diagnosis. Recent software programs are comparable with the conventional human scoring method regarding detectability of the radiographic progression of RA. Thus, automatic and accurate selection
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Application of texture analysis based on T2-weighted magnetic resonance images in discriminating Gleason scores of prostate cancer. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-08-31 Ruigen Pan,Xueli Yang,Zhenyu Shu,Yifeng Gu,Lihua Weng,Yuezhu Jia,Jianju Feng
OBJECTIVE:To investigate the value of texture analysis in magnetic resonance images for the evaluation of Gleason scores (GS) of prostate cancer. METHODS:Sixty-six prostate cancer patients are retrospective enrolled, which are divided into five groups namely, GS = 6, 3 + 4, 4 + 3, 8 and 9–10 according to postoperative pathological results. Extraction and analysis of texture features in T2-weighted
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Diagnostic performance on multiple parameters of real-time ultrasound shear wave elastography for evaluating nonalcoholic fatty liver disease: A rabbit model. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-08-31 Pinggui Lei,Piaochen Zhang,Qianijao Liu,Yan Wang,Pingxian Wang,Qinghong Duan,Jing Liu,Shi Zhou,Wei Qian,Jun Jiao
OBJECTIVE:To study the diagnostic value of real-time ultrasound shear wave elastography (US-SWE) in evaluating the histological stages of nonalcoholic fatty liver disease (NAFLD) in a rabbit model. MATERIALS AND METHODS:Twenty-one 8-week-old rabbits were fed a high-fat, high-cholesterol diet (experimental groups), and seven rabbits were fed a standard diet (control group). All rabbits underwent real-time
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Evaluation of dynamic lung changes during coronavirus disease 2019 (COVID-19) by quantitative computed tomography. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-08-17 Cong Shen,Nan Yu,Shubo Cai,Jie Zhou,Jiexin Sheng,Kang Liu,Heping Zhou,Youmin Guo
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%)
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Recognition of calcifications in thyroid nodules based on attention-gated collaborative supervision network of ultrasound images. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-08-12 Liqun Zhang,Ke Chen,Lin Han,Yan Zhuang,Zhan Hua,Cheng Li,Jiangli Lin
BACKGROUND:Calcification is an important criterion for classification between benign and malignant thyroid nodules. Deep learning provides an important means for automatic calcification recognition, but it is tedious to annotate pixel-level labels for calcifications with various morphologies. OBJECTIVE:This study aims to improve accuracy of calcification recognition and prediction of its location,
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Retrospective 3D analysis of bone regeneration after cystectomy of odontogenic cysts. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-08-12 Buchbender Mayte,Koch Birte,Kesting Marco Rainer,Matta Ragai Edward,Adler Werner,Seidel Anna,Schmitt Christian Martin
BACKGROUND/OBJECTIVE:In this retrospective study, we aimed to investigate a new 3D evaluation method for evaluating bone regeneration after cystectomy of odontogenic cysts. METHODS:The study included 26 patients who underwent cystectomies between 2012 and 2017 and had received either fillings or non-fillings with autologous iliac crest. Bony regeneration was analyzed using 3D imaging software and comparing
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Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-08-11 Saleh Albahli,Waleed Albattah
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
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A retrospective study of the initial chest CT imaging findings in 50 COVID-19 patients stratified by gender and age. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-08-06 Qianbiao Gu,Xin Ouyang,An Xie,Xianzheng Tan,Jianbin Liu,Feng Huang,Peng Liu
OBJECTIVE:To retrospectively analyze and stratify the initial clinical features and 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:
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Hybrid clustering system using Nystagmus parameters discrimination for vestibular disorder diagnosis. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-07-31 Amine Ben Slama,Hanene Sahli,Aymen Mouelhi,Jihene Marrakchi,Seif Boukriba,Hedi Trabelsi,Mounir Sayadi
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
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Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-07-31 Md Mamunur Rahaman,Chen Li,Yudong Yao,Frank Kulwa,Mohammad Asadur Rahman,Qian Wang,Shouliang Qi,Fanjie Kong,Xuemin Zhu,Xin Zhao
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 significantstep to fight against this virus as well as release pressure off the healthcare system. OBJECTIVE:One
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Review of CT image reconstruction open source toolkits. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-08-01 Liu Shi,Baodong Liu,Hengyong Yu,Cunfeng Wei,Long Wei,Li Zeng,Ge Wang
Computed tomography (CT) has been widely applied in medical diagnosis, nondestructive evaluation, homeland security, and other science and engineering applications. Image reconstruction is one of the core CT imaging technologies. In this review paper, we systematically reviewed the currently publicly available CT image reconstruction open source toolkits in the aspects of their environments, object
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Dosimetric evaluation of PASSAG-U polymer gel dosimeter: Dependence of dose rate and photon energy. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-08-01 Bagher Farhood,Kamal Mohammadi Asl,Mostafa Sarvizadeh,Akbar Aliasgharzadeh
OBJECTIVE:Several physical factors such as dose rate and photon energy may change response and sensitivity of polymer gel dosimeters. This study aims to evaluate the R2-dose response and sensitivity dependence of PASSAG-U gel dosimeters with 3% and 5% urea on dose rate and photon energy. MATERIALSAND METHODS:The PASSAG-U gel dosimeters were prepared under normal atmospheric conditions. The obtained
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DCU-Net: Multi-scale U-Net for brain tumor segmentation. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-08-01 Tiejun Yang,Yudan Zhou,Lei Li,Chunhua Zhu
BACKGROUND:Brain tumor segmentation plays an important role in assisting diagnosis of disease, treatment plan planning, and surgical navigation. OBJECTIVE:This study aims to improve the accuracy of tumor boundary segmentation using the multi-scale U-Net network. METHODS:In this study, a novel U-Netwith dilated convolution (DCU-Net) structure is proposed for brain tumor segmentation based on the classic
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Breast cancer pathological image classification based on deep learning. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-08-01 Yubao Hou
The automatic classification of breast cancer pathological images has important clinical application value. However, to develop the classification algorithm using the artificially extracted image features faces several challenges including the requirement of professional domain knowledge to extractand compute highiquality image features, which are often time-consuming, laborious, and difficult. For
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Questionable necessity of nitroglycerin for diagnostic coronary artery examination using 320-row multi-detector computed tomography. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-07-29 Nan-Han Lu,Yi-Shan Liu,Ko-In Liu,Shih-Yen Hsu,Yung-Hui Huang,Cheuk-Kwan Sun,Tai-Been Chen
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
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Dynamic CT assessment of disease change and prognosis of patients with moderate COVID-19 pneumonia. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-07-29 Hua Zhang,Xiaohong Liu,Peng Yu,Mingyuan Cheng,Weiting Wang,Yipeng Sun,Bingliang Zeng,Bing Fan
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
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Effect of iterative reconstruction algorithm levels on noise index and figure-of-merit in CT pulmonary angiography examinations. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-07-24 H H Harun,M K A Karim,Z Abbas,A Sabarudin,S C Muniandy,M J Ibahim
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
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Fusion of 3-D medical image gradient domain based on detail-driven and directional structure tensor. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-07-14 Yu Wang,Yuanjun Wang
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
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Tailoring steroids in the treatment of COVID-19 pneumonia assisted by CT scans: Three case reports. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-07-14 Ying Su,Yi Han,Jie Liu,Yue Qiu,Qian Tan,Zhen Zhou,Yi-Zhou Yu,Jun Chen,Maryellen L Giger,Fleming Y M Lure,Zhe Luo
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)
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L3-subshell alignment of Ag in collision with 15 keV electrons. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-07-14 Pengfei Hu,Xing Wang
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°
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Developing and verifying automatic detection of active pulmonary tuberculosis from multi-slice spiral CT images based on deep learning. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-07-06 Luyao Ma,Yun Wang,Lin Guo,Yu Zhang,Ping Wang,Xu Pei,Lingjun Qian,Stefan Jaeger,Xiaowen Ke,Xiaoping Yin,Fleming Y M Lure
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
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Diagnosis of osteoporosis using modified U-net architecture with attention unit in DEXA and X-ray images. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-07-06 S M Nazia Fathima,R Tamilselvi,M Parisa Beham,D Sabarinathan
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,
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An enhanced OCT image captioning system to assist ophthalmologists detecting and classifying eye diseases. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-06-22 Sivamurugan Vellakani,Indumathi Pushbam
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
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Statistical image-based material decomposition for triple-energy computed tomography using total variation regularization. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-06-22 Shanzhou Niu,Shaohui Lu,You Zhang,Xiaokun Huang,Yuncheng Zhong,Gaohang Yu,Jing Wang
BACKGROUND:Triple-energy computed tomography (TECT) can obtain x-ray attenuation measurements at three energy spectra, thereby allowing identification of different material compositions with same or very similar attenuation coefficients. This ability is known as material decomposition, which can decompose TECT images into different basis material image. However, the basis material image would be severely
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Assessment of image quality and dose in contrast-enhanced head and neck CT angiography of New Zealand rabbit. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-06-22 Chia-Chi Hsiao,Po-Chou Chen,Pei-Chi Kuo,Chih-Hao Ho,Jo-Chi Jao
BACKGROUND:Although computed tomography (CT) is a powerful diagnostic imaging modality for diagnosing vascular diseases, it is some what risky to human health due to the high radiation dosage. Thus, CT vendors have developed low dose computed tomography (LDCT) aiming to solve this problem. Nowadays, LDCT has gradually become a main stream of CT examination. OBJECTIVE:This study aimed to assess the
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A radiomics signature to identify malignant and benign liver tumors on plain CT images. J. X-Ray Sci. Technol. (IF 1.342) Pub Date : 2020-06-18 Jin Yin,Jia-Jun Qiu,Wei Qian,Lin Ji,Dan Yang,Jing-Wen Jiang,Jun-Ren Wang,Lan Lan
BACKGROUND:In regular examinations, it may be difficult to visually identify benign and malignant liver tumors based on plain computed tomography (CT) images. RCAD (radiomics-based computer-aided diagnosis) has proven to be helpful and provide interpretability in clinical use. OBJECTIVE:This work aims to develop a CT-based radiomics signature and investigate its correlation with malignant/benign liver
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