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Predicting patient-specific organ doses from thoracic CT examinations using support vector regression algorithm J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-04-08 Wencheng Shao, Xin Lin, Ying Huang, Liangyong Qu, Zhuo Weihai , Haikuan Liu
PURPOSE: This study aims to propose and develop a fast, accurate, and robust prediction method of patient-specific organ doses from CT examinations using minimized computational resources. MATERIALS AND METHODS: We randomly selected the image data of 723 patients who underwent thoracic CT examinations. We performed auto-segmentation based on the selected data to generate the regions of interest (ROIs)
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DDA-SSNets: Dual decoder attention-based semantic segmentation networks for COVID-19 infection segmentation and classification using chest X-Ray images J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-04-06 Anandbabu Gopatoti, Ramya Jayakumar, Poornaiah Billa, Vijayalakshmi Patteeswaran
BACKGROUND: COVID-19 needs to be diagnosed and staged to be treated accurately. However, prior studies’ diagnostic and staging abilities for COVID-19 infection needed to be improved. Therefore, new deep learning-based approaches are required to aid radiologists in detecting and quantifying COVID-19-related lung infections. OBJECTIVE: To develop deep learning-based models to classify and quantify COVID-19-related
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SADSNet: A robust 3D synchronous segmentation network for liver and liver tumors based on spatial attention mechanism and deep supervision J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-03-27 Sijing Yang, Yongbo Liang, Shang Wu, Peng Sun, Zhencheng Chen
Highlights• Introduce a data augmentation strategy to expand the required different morphological data during the training and learning phase, and improve the algorithm’s feature learning ability for complex and diverse tumor morphology CT images.• Design attention mechanisms for encoding and decoding paths to extract fine pixel level features, improve feature extraction capabilities, and achieve efficient
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A dense and U-shaped transformer with dual-domain multi-loss function for sparse-view CT reconstruction J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-03-27 Peng Liu, Chenyun Fang, Zhiwei Qiao
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
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A dual-energy CT reconstruction method based on anchor network from dual quarter scans J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-03-27 Junru Ren, Wenkun Zhang, YiZhong Wang, Ningning Liang, Linyuan Wang, Ailong Cai, Shaoyu Wang, Zhizhong Zheng, Lei Li, Bin Yan
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 researchto promote the applications of DECT in wide range areas and reduce the radiation dose as low as reasonably
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Severity-stratification of interstitial lung disease by deep learning enabled assessment and quantification of lesion indicators from HRCT images J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-03-27 Yexin Lai, Xueyu Liu, Fan Hou, Zhiyong Han, Linning E, Ningling Su, Dianrong Du, Zhichong Wang, Wen Zheng, Yongfei Wu
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 thehigh inter- and intra-subjective observer variability. OBJECTIVE: To solve these problems, in this work
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Three-dimensional analysis of puncture needle path through safety triangle approach PLD and design of puncture positioning guide plate J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-03-20 Penghui Yu, Yanbing Li, Qidong Zhao, Xia Chen, Liqin Wu, Shuai Jiang, Libing Rao, Yihua Rao
OBJECTIVE:In this study, the three-dimensional relationship between the optimal puncture needle path and the lumbar spinous process was discussed using digital technology. Additionally, the positioning guide plate was designed and 3D printed in order to simulate the surgical puncture of specimens.This plate served as an important reference for the preoperative simulation and clinical application of
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Anatomical changes and dosimetric analysis of the neck region based on FBCT for nasopharyngeal carcinoma patients during radiotherapy J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-03-06 Aoqiang Chen, Xuemei Chen, Xiaobo Jiang, Yajuan Wang, Feng Chi, Dehuan Xie, Meijuan Zhou
BACKGROUND:The study aimed to investigate anatomical changes in the neck region and their impact on dose distribution in patients with nasopharyngeal carcinoma (NPC) undergoing intensity modulated radiation therapy (IMRT), as well as to determine the optimal time for replanning during treatment. METHODS:Twenty NPC patients received IMRT with weekly pretreatment in-room kV fan beam computed tomography
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High-resolution X-Ray imaging of small animal samples based on Commercial-Off-The-Shelf CMOS image sensors J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-02-27 MartÍn Pérez, Gerardo M. Lado, Germán Mato, Diego G. Franco, Ignacio Artola Vinciguerra, Mariano Gómez Berisso, Federico J. Pomiro, José Lipovetzky, Luciano Marpegan
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
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Machine learning framework for simulation of artifacts in paranasal sinuses diagnosis using CT images J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-02-22 Abdullah Musleh
In the medical field, diagnostic tools that make use of deep neural networks have reached a level of performance never before seen. A proper diagnosis of a patient’s condition is crucial in modern medicine since it determines whether or not the patient will receive the care they need. Data from a sinus CT scan is uploaded to a computer and displayed on a high-definition monitor to give the surgeon
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Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-02-21 Haoyan Li, Zhentao Li, Shuaiyi Gao, Jiaqi Hu, Zhihao Yang, Yun Peng, Jihang Sun
OBJECTIVES:To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction-V (ASIR-V) algorithms. METHODS:An ACR464 phantom was scanned with DEsCT at four dose levels (3.5 mGy, 5 mGy, 7.5 mGy
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A hybrid thyroid tumor type classification system using feature fusion, multilayer perceptron and bonobo optimization J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-02-21 B. Shankarlal, S. Dhivya, K. Rajesh, S. Ashok
BACKGROUND:Thyroid tumor is considered to be a very rare form of cancer. But recent researches and surveys highlight the fact that it is becoming prevalent these days because of various factors. OBJECTIVES:This paper proposes a novel hybrid classification system that is able to identify and classify the above said four different types of thyroid tumors using high end artificial intelligence techniques
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Lossless compression-based detection of osteoporosis using bone X-ray imaging J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-02-20 Khalaf Alshamrani, Hassan A. Alshamrani
BACKGROUND: Digital X-ray imaging is essential for diagnosing osteoporosis, but distinguishing affected patients from healthy individuals using these images remains challenging. OBJECTIVE: This study introduces a novel method using deep learning to improve osteoporosis diagnosis from bone X-ray images. METHODS: A dataset of bone X-ray images was analyzed using a newly proposed procedure. This procedure
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Peri-lesion regions in differentiating suspicious breast calcification-only lesions specifically on contrast enhanced mammography J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-29 Kun Cao, Fei Gao, Rong Long, Fan-Dong Zhang, Chen-Cui Huang, Min Cao, Yi-Zhou Yu, Ying-Shi Sun
PURPOSE: The explore the added value of peri-calcification regions on contrast-enhanced mammography (CEM) in the differential diagnosis of breast lesions presenting as only calcification on routine mammogram. METHODS: Patients who underwent CEM because of suspicious calcification-only lesions wereincluded. The test set included patients between March 2017 and March 2019, while the validation set was
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Classification of benign and malignant pulmonary nodule based on local-global hybrid network J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-24 Xin Zhang, Ping Yang, Ji Tian, Fan Wen, Xi Chen, Tayyab Muhammad
BACKGROUND:The accurate classification of pulmonary nodules has great application value in assisting doctors in diagnosing conditions and meeting clinical needs. However, the complexity and heterogeneity of pulmonary nodules make it difficult to extract valuable characteristics of pulmonary nodules, so it is still challenging to achieve high-accuracy classification of pulmonary nodules. OBJECTIVE:In
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Approaches for Stereotactic Radiosurgery (SRS)/Stereotactic Radiotherapy (SRT) in brain metastases using different radiotherapy modalities (Feasibility study) J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-22 Zyad A. Tawfik, Mohamed El-Azab Farid, Khaled M. El Shahat, Ahmed A. Hussein, Mostafa Al Etreby
BACKGROUND: SRS and SRT are precise treatments for brain metastases, delivering high doses while minimizing doses to nearby organs. Modern linear accelerators enable the precise delivery of SRS/SRT using different modalities like three-dimensional conformal radiotherapy (3DCRT), intensity-modulatedradiotherapy (IMRT), and Rapid Arc (RA). OBJECTIVE: This study aims to compare dosimetric differences
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Multimodal feature fusion in deep learning for comprehensive dental condition classification J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-12 Shang-Ting Hsieh, Ya-Ai Cheng
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
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Comparative study of abdominal CT enhancement in overweight and obese patients based on different scanning modes combined with different contrast medium concentrations J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-12 Kai Gao, Ze-Peng Ma, Tian-Le Zhang, Yi-Wen Liu, Yong-Xia Zhao
PURPOSE: To compare image quality, iodine intake, and radiation dose in overweight and obese patients undergoing abdominal computed tomography (CT) enhancement using different scanning modes and contrast medium. METHODS: Ninety overweight and obese patients (25 kg/m2≤body mass index (BMI)< 30 kg/m2 and BMI≥30 kg/m2) who underwent abdominal CT-enhanced examinations were randomized into three groups
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Dosimetry and treatment efficiency of SBRT using TaiChiB radiotherapy system for two-lung lesions with one overlapping organs at risk J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-12 Yanhua Duan, Aihui Feng, Hao Wang, Hua Chen, Hengle Gu, Yan Shao, Ying Huang, Zhenjiong Shen, Qing Kong, Zhiyong Xu
Purpose:This study aims to assess the dosimetry and treatment efficiency of TaiChiB-based Stereotactic Body Radiotherapy (SBRT) plans applying to treat two-lung lesions with one overlapping organs at risk. Methods:For four retrospective patients diagnosed with two-lung lesions each patient, four treatment plans were designed including Plan Edge, TaiChiB linac-based, RGS-based, and a linac-RGS hybrid
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Photothermal effect in X-ray images for computed tomography of metallic parts: Stainless steel spheres J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-09 Verena M. Moock, Darien E. Arce Chávez, Crescencio García-Segundo, Leopoldo Ruiz-Huerta
Abstract BACKGROUND: The environmental impact on industrial X-ray tomography systems has gained its attention in terms of image precision and metrology over recent years, yet is still complex due to the variety of applications. OBJECTIVE: The current study explores the photothermal repercussions of the overall radiation exposure time. It shows the emerging dimensional uncertainty when measuring a stainless
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Semi-supervised segmentation of metal-artifact contaminated industrial CT images using improved CycleGAN J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-06 Shi Bo Jiang, Yue Wen Sun, Shuo Xu, Hua Xia Zhang, Zhi Fang Wu
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
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Multi-parametric assessment of cardiac magnetic resonance images to distinguish myocardial infarctions: A tensor-based radiomics feature J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-06 Dehua Wang, Hayder Jasim Taher, Murtadha Al-Fatlawi, Badr Ahmed Abdullah, Munojat Khayatovna Ismailova, Razzagh Abedi-Firouzjah
AIM:This study assessed the myocardial infarction (MI) using a novel fusion approach (multi-flavored or tensor-based) of multi-parametric cardiac magnetic resonance imaging (CMRI) at four sequences; T1-weighted (T1W) in the axial plane, sense-balanced turbo field echo (sBTFE) in the axial plane, late gadolinium enhancement of heart short axis (LGE-SA) in the sagittal plane, and four-chamber views of
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An adaptive weighted ensemble learning network for diabetic retinopathy classification J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-06 Panpan Wu, Yue Qu, Ziping Zhao, Yue Cui, Yurou Xu, Peng An, Hengyong Yu
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
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Deep-silicon photon-counting x-ray projection denoising through reinforcement learning J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-06 Md Sayed Tanveer, Christopher Wiedeman, Mengzhou Li, Yongyi Shi, Bruno De Man, Jonathan S. Maltz, Ge Wang
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
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The investigation of dose rate and photon beam energy dependence of optimized PASSAG polymer gel dosimeter using magnetic resonance imaging J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-06 Bo Liu, Shaima Haithem Zaki, Eduardo García, Amanda Bonilla, Daha Thabit, Aya Hussein Adab
OBJECTIVE:It seems that dose rate (DR) and photon beam energy (PBE) may influence the sensitivity and response of polymer gel dosimeters. In the current project, the sensitivity and response dependence of optimized PASSAG gel dosimeter (OPGD) on DR and PBE were assessed. MATERIALS AND METHODS:We fabricated the OPGD and the gel samples were irradiated with various DRs and PBEs. Then, the sensitivity
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Application of dose-gradient function in reducing radiation induced lung injury in breast cancer radiotherapy J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-05 Han Bai, Hui Song, Qianyan Li, Jie Bai, Ru Wang, Xuhong Liu, Feihu Chen, Xiang Pan
OBJECTIVE:Try to create a dose gradient function (DGF) and test its effectiveness in reducing radiation induced lung injury in breast cancer radiotherapy. MATERIALS AND METHODS:Radiotherapy plans of 30 patients after breast-conserving surgery were included in the study. The dose gradient function was defined as DGH=VDVp3 , then the area under the DGF curve of each plan was calculated in rectangular
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Diagnosis of Covid-19 from CT slices using Whale Optimization Algorithm, Support Vector Machine and Multi-Layer Perceptron J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-05 R. Betshrine Rachel, H. Khanna Nehemiah, Vaibhav Kumar Singh, Rebecca Mercy Victoria Manoharan
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 fromchest Computed Tomography (CT) slices is modelled and experimented. METHODS:The lung tissues are segmented
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Artificial intelligence auxiliary diagnosis and treatment system for breast cancer in developing countries J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-05 Wenxiu Li, Fangfang Gou, Jia Wu
BACKGROUND:In many developing countries, a significant number of breast cancer patients are unable to receive timely treatment due to a large population base, high patient numbers, and limited medical resources. OBJECTIVE:This paper proposes a breast cancer assisted diagnosis system based on electronic medical records. The goal of this system is to address the limitations of existing systems, which
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A fast response time gas ionization chamber detector with a grid structure J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-05 Jiahao Chang, Chaoyang Zhu, Yuanpeng Song, Zhentao Wang
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
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The mechanism of moire artifacts in single-grating imaging systems and image quality optimization J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-05 Fangke Zong, Jun Yang, Jun Jiang, JinChuan Guo
In the X-ray single-grating imaging system, the acquisition of frequency information is the key step of phase-contrast and scattering information recovery. In the process of information extraction, it is easy to lead to the degradation of imaging quality due to the Moire Artifact, thus limiting thedevelopment and application of X-ray single-grating imaging system. In order to address the above problems
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Decomposition iteration strategy for low-dose CT denoising J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-05 Zhiyuan Li, Yi Liu, Pengcheng Zhang, Jing Lu, Zhiguo Gui
In the medical field, computed tomography (CT) is a commonly used examination method, but the radiation generated increases the risk of illness in patients. Therefore, low-dose scanning schemes have attracted attention, in which noise reduction is essential. We propose a purposeful and interpretable decomposition iterative network (DISN) for low-dose CT denoising. This method aims to make the network
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Nomograms combining computed tomography-based body composition changes with clinical prognostic factors to predict survival in locally advanced cervical cancer patients J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-05 Baoyue Fu, Longyu Wei, Chuanbin Wang, Baizhu Xiong, Juan Bo, Xueyan Jiang, Yu Zhang, Haodong Jia, Jiangning Dong
OBJECTIVE:To explore the value of body composition changes (BCC) measured by quantitative computed tomography (QCT) for evaluating the survival of patients with locally advanced cervical cancer (LACC) underwent concurrent chemoradiotherapy (CCRT), nomograms combined BCC with clinical prognostic factors (CPF) were constructed to predict overall survival (OS) and progression-free survival (PFS). METHODS:Eighty-eight
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Research on breast cancer pathological image classification method based on wavelet transform and YOLOv8 J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-05 Yunfeng Yang, Jiaqi Wang
Breast cancer is one of the cancers with high morbidity and mortality in the world, which is a serious threat to the health of women. With the development of deep learning, the recognition about computer-aided diagnosis technology is getting higher and higher. And the traditional data feature extraction technology has been gradually replaced by the feature extraction technology based on convolutional
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Reduction of overfitting on the highly imbalanced ISIC-2019 skin dataset using deep learning frameworks J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-12-30 Erapaneni Gayatri, S.L. Aarthy
BACKGROUND:With the rapid growth of Deep Neural Networks (DNN) and Computer-Aided Diagnosis (CAD), more significant works have been analysed for cancer related diseases. Skin cancer is the most hazardous type of cancer that cannot be diagnosed in the early stages. OBJECTIVE:The diagnosis of skin cancer is becoming a challenge to dermatologists as an abnormal lesion looks like an ordinary nevus at the
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Performance evaluation of quantitative material decomposition in slow kVp switching dual-energy CT J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-12-30 Chenchen Ma, Ting Su, Jiongtao Zhu, Xin Zhang, Hairong Zheng, Dong Liang, Na Wang, Yunxin Zhang, Yongshuai Ge
BACKGROUND:Slow kVp switching technique is an important approach to realize dual-energy CT (DECT) imaging, but its performance has not been thoroughly investigated yet. OBJECTIVE:This study aims at comparing and evaluating the DECT imaging performance of different slow kVp switching protocols, andthus helps determining the optimal system settings. METHODS:To investigate the impact of energy separation
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A novel multi-dimensional coal and gangue X-ray sorting algorithm based on CdZnTe photon counting detectors J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-03 Yang Kang, Rui Wu, Peizheng Li, Qingpei Li, Sen Wu, Tingting Tan, Yingrui Li, Gangqiang Zha
BACKGROUND: The gangue content in coal seriously affects the calorific value produced by its combustion. In practical applications, gangue in coal needs to be completely separated. The pseudo-dual-energy X-ray method does not have high sorting accuracy. OBJECTIVE: This study aims to propose a novelmulti-dimensional coal and gangue X-ray sorting algorithm based on CdZnTe photon counting detectors to
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Diagnostic reference levels in spinal CT: Jordanian assessments and global benchmarks J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2024-01-03 Mohammad Rawashdeh, Abdel-Baset Bani Yaseen, Mark McEntee, Andrew England, Praveen Kumar, Charbel Saade
BACKGROUND: To reduce radiation dose and subsequent risks, several legislative documents in different countries describe the need for Diagnostic Reference Levels (DRLs). Spinal radiography is a common and high-dose examination. Therefore, the aim of this work was to establish the DRL for Computed Tomography (CT) examinations of the spine in healthcare institutions across Jordan. METHODS: Data was retrieved
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A deep learning and radiomics based Alberta stroke program early CT score method on CTA to evaluate acute ischemic stroke J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-11-17 Ting Fang, Naijia Liu, Shengdong Nie, Shouqiang Jia, Xiaodan Ye
BACKGROUND: Alberta stroke program early CT score (ASPECTS) is a semi-quantitative evaluation method used to evaluate early ischemic changes in patients with acute ischemic stroke, which can guide physicians in treatment decisions and prognostic judgments. OBJECTIVE: We propose a method combining deep learning and radiomics to alleviate the problem of large inter-observer variance in ASPECTS faced
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Multiple energy X-ray imaging of metal oxide particles inside gingival tissues J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-11-10 Jarrod Cortez, Ignacio Romero, Jason Ngo, Md Sayed Tanveer Azam, Chuang Niu, Cássio Luiz Coutinho Almeida-da-Silva, Leticia Ferreira Cabido, David M. Ojcius, Wei-Chun Chin, Ge Wang, Changqing Li
BACKGROUND:Periodontal disease affects over 50% of the global population and is characterized by gingivitis as the initial sign. One dental health issue that may contribute to the development of periodontal disease is foreign body gingivitis (FBG), which can result from exposure to some kinds of foreign metal particles from dental products or food. OBJECTIVE:We design a novel, portable, affordable
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APNet: Adaptive projection network for medical image denoising J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-10-31 Qiyi Song, Xiang Li, Mingbao Zhang, Xiangyi Zhang, Dang N.H. Thanh
BACKGROUND:In clinical medicine, low-dose radiographic image noise reduces the quality of the detected image features and may have a negative impact on disease diagnosis. OBJECTIVE:In this study, Adaptive Projection Network (APNet) is proposed to reduce noise from low-dose medical images. METHODS:APNet is developed based on an architecture of the U-shaped network to capture multi-scale data and achieve
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Fusion extracted features from deep learning for identification of multiple positioning errors in dental panoramic imaging J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-10-09 Hsin-Yueh Su, Shang-Ting Hsieh, Kun-Zhe Tsai, Yu-Li Wang, Chi-Yuan Wang, Shih-Yen Hsu, Kuo-Ying Liu, Yung-Hui Huang, Ya-Wen Wei, Nan-Han Lu, Tai-Been Chen
BACKGROUND:Dental panoramic imaging plays a pivotal role in dentistry for diagnosis and treatment planning. However, correctly positioning patients can be challenging for technicians due to the complexity of the imaging equipment and variations in patient anatomy, leading to positioning errors. These errors can compromise image quality and potentially result in misdiagnoses. OBJECTIVE:This research
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A novel fusion method for X-ray phase contrast imaging based on fast adaptive bidimensional empirical mode decomposition J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-10-09 Zonghan Tian, Siwei Tao, Ling Bai, Yueshu Xu, Xu Liu, Cuifang Kuang
BACKGROUNDS:X-ray phase contrast imaging (XPCI) can separate the attenuation, refraction, and scattering signals of the object. The application of image fusion enables the concentration of distinctive information into a single image. OBJECTIVE:To explore the application value of a novel image fusion method for a XPCI system and a computed tomography (CT) system. METHODS:The means of fast adaptive bidimensional
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Application of radiomics based on chest CT-enhanced dual-phase imaging in the immunotherapy of non-small cell lung cancer J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-10-06 Ze-Peng Ma, Xiao-Lei Li, Kai Gao, Tian-Le Zhang, Heng-Di Wang, Yong-Xia Zhao
OBJECTIVE:To explore the value of applying computed tomography (CT) radiomics based on different CT-enhanced phases to determine the immunotherapeutic efficacy of non-small cell lung cancer (NSCLC). METHODS:106 patients with NSCLC who underwent immunotherapy are randomly divided into training (74)and validation (32) groups. CT-enhanced arterial and venous phase images of patients before treatment are
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A densely connected LDCT image denoising network based on dual-edge extraction and multi-scale attention under compound loss J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-09-19 Lina Jia, Xu He, Aimin Huang, Beibei Jia, Zhiguo Gui
BACKGROUND:Low dose computed tomography (LDCT) uses lower radiation dose, but the reconstructed images contain higher noise that can have negative impact in disease diagnosis. Although deep learning with the edge extraction operators reserves edge information well, only applying the edge extractionoperators to input LDCT images does not yield overall satisfactory results. OBJECTIVE:To improve LDCT
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MTAN: A semi-supervised learning model for kidney tumor segmentation J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-09-14 Peng Sun, Sijing Yang, Haolin Guan, Taiping Mo, Bonan Yu, Zhencheng Chen
BACKGROUND:Medical image segmentation is crucial in disease diagnosis and treatment planning. Deep learning (DL) techniques have shown promise. However, optimizing DL models requires setting numerous parameters, and demands substantial labeled datasets, which are labor-intensive to create. OBJECTIVE:This study proposes a semi-supervised model that can utilize labeled and unlabeled data to accurately
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CGP-Uformer: A low-dose CT image denoising Uformer based on channel graph perception J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-09-12 Huimin Yan, Chenyun Fang, Peng Liu, Zhiwei Qiao
BACKGROUND:An effective method for achieving low-dose CT is to keep the number of projection angles constant while reducing radiation dose at each angle. However, this leads to high-intensity noise in the reconstructed image, adversely affecting subsequent image processing, analysis, and diagnosis.OBJECTIVE:This paper proposes a novel Channel Graph Perception based U-shaped Transformer (CGP-Uformer)
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Analytical reconstructions of full-scan multiple source-translation computed tomography under large field of views J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-09-12 Zhisheng Wang, Yue Liu, Shunli Wang, Xingyuan Bian, Zongfeng Li, Junning Cui
This paper is to investigate the high-quality analytical reconstructions of multiple source-translation computed tomography (mSTCT) under an extended field of view (FOV). Under the larger FOVs, the previously proposed backprojection filtration (BPF) algorithms for mSTCT, including D-BPF and S-BPF (their differences are different derivate directions along the detector and source, respectively), make
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Enhancement based convolutional dictionary network with adaptive window for low-dose CT denoising J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-09-02 Yi Liu, Rongbiao Yan, Yuhang Liu, Pengcheng Zhang, Yang Chen, Zhiguo Gui
BACKGROUND:Recently, one promising approach to suppress noise/artifacts in low-dose CT (LDCT) images is the CNN-based approach, which learns the mapping function from LDCT to normal-dose CT (NDCT). However, most CNN-based methods are purely data-driven, thus lacking sufficient interpretability andoften losing details. OBJECTIVE:To solve this problem, we propose a deep convolutional dictionary learning
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Dual-energy micro-focus computed tomography based on the energy-angle correlation of inverse Compton scattering source J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-08-22 Yue Ma, Dexiang Liu, Jianfei Hua, Wei Lu
BACKGROUND:Inverse Compton scattering (ICS) source can produce quasi-monoenergetic micro-focus X-rays ranging from keV to MeV level, with potential applications in the field of high-resolution computed tomography (CT) imaging. ICS source has an energy-angle correlated feature that lower photon energy is obtained at larger emission angle, thus different photon energies are inherently contained in each
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CT imaging-based radiomics signatures improve prognosis prediction in postoperative colorectal cancer J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-08-18 Yan Kong, Muchen Xu, Xianding Wei, Danqi Qian, Yuan Yin, Zhaohui Huang, Wenchao Gu, Leyuan Zhou
OBJECTIVE:To investigate the use of non-contrast-enhanced (NCE) and contrast-enhanced (CE) CT radiomics signatures (Rad-scores) as prognostic factors to help improve the prediction of the overall survival (OS) of postoperative colorectal cancer (CRC) patients. METHODS:A retrospective analysis was performed on 65 CRC patients who underwent surgical resection in our hospital as the training set, and
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Dual-modal radiomics for predicting cervical lymph node metastasis in papillary thyroid carcinoma J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-08-17 Yongzhen Ren, Siyuan Lu, Dongmei Zhang, Xian Wang, Enock Adjei Agyekum, Jin Zhang, Qing Zhang, Feiju Xu, Guoliang Zhang, Yu Chen, Xiangjun Shen, Xuelin Zhang, Ting Wu, Hui Hu, Xiuhong Shan, Jun Wang, Xiaoqin Qian
BACKGROUND:Preoperative prediction of cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) is significant for surgical decision-making. OBJECTIVE:This study aims to develop a dual-modal radiomics (DMR) model based on grayscale ultrasound (GSUS) and dual-energy computed tomography (DECT) for non-invasive CLNM in PTC. METHODS:In this study, 348 patients with pathologically
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Classification of esophageal cancer stage using an ensembled CNN with artificial bee colony optimization method and a SVM classifier J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-08-14 A. Chempak Kumar, D. Muhammad Noorul Mubarak
BACKGROUND:Esophageal cancer (EC) is aggressive cancer with a high fatality rate and a rapid rise of the incidence globally. However, early diagnosis of EC remains a challenging task for clinicians. OBJECTIVE:To help address and overcome this challenge, this study aims to develop and test a new computer-aided diagnosis (CAD) network that combines several machine learning models and optimization methods
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Development of a new body weight estimation method using head CT scout images J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-07-31 Tatsuya Kondo, Manami Umezu, Yohan Kondo, Mitsuru Sato, Tsutomu Kanazawa, Yoshiyuki Noto
BACKGROUND:Imaging examinations are crucial for diagnosing acute ischemic stroke, and knowledge of a patient’s body weight is necessary for safe examination. To perform examinations safely and rapidly, estimating body weight using head computed tomography (CT) scout images can be useful. OBJECTIVE:This study aims to develop a new method for estimating body weight using head CT scout images for contrast-enhanced
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Enhancing teeth segmentation using multifusion deep neural net in panoramic X-ray images J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-07-21 Saurabh Arora, Ruchir Gupta, Rajeev Srivastava
BACKGROUND:Precise teeth segmentation from dental panoramic X-ray images is an important task in dental practice. However, several issues including poor image contrast, blurring borders of teeth, presence of jaw bones and other mouth elements, makes reading and examining such images a challenging and time-consuming task for dentists. Thus, developing a precise and automated segmentation technique is
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Automated recognition of the major muscle injury in athletes on X-ray CT images 1 J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-07-21 Wanping Jia, Guangyong Zhao
Background:In this research, imaging techniques such as CT and X-ray are used to locate important muscles in the shoulders and legs. Athletes who participate in sports that require running, jumping, or throwing are more likely to get injuries such as sprains, strains, tendinitis, fractures, and dislocations. One proposed automated technique has the overarching goal of enhancing recognition. Objective:This
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Accelerating image reconstruction for multi-contrast MRI based on Y-Net3+ J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-07-13 Xin Cai, Xuewen Hou, Rong Sun, Xiao Chang, Honglin Zhu, Shouqiang Jia, Shengdong Nie
BACKGROUND:As one of the significant preoperative imaging modalities in medical diagnosis, Magnetic resonance imaging (MRI) takes a long scanning time due to its special imaging principle. OBJECTIVE:We propose an innovative MRI reconstruction strategy and data consistency method based on deep learning to reconstruct high-quality brain MRIs from down-sampled data and accelerate the MR imaging process
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A computational approach for analysis of intratumoral heterogeneity and standardized uptake value in PET/CT images 1 J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-07-13 Khalaf Alshamrani, Hassan A. Alshamrani
BACKGROUND:By providing both functional and anatomical information from a single scan, digital imaging technologies like PET/CT and PET/MRI hybrids are gaining popularity in medical imaging industry. In clinical practice, the median value (SUVmed) receives less attention owing to disagreements surrounding what defines a lesion, but the SUVmax value, which is a semi-quantitative statistic used to analyse
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Differences in apparent diffusion coefficient histogram analysis according to EGFR mutation status in brain metastasis due to lung adenocarcinoma J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-07-06 Ezel Yaltırık Bilgin, Özkan Ünal, Muhammed Fatih Göç, Taha Bahşi
BACKGROUND:The etiology, clinicopathological features, and prognosis of cancer in cases with EGFR mutations are different from those without mutations. OBJECTİVE:This study aims to evaluate the differences in ADC histogram analysis in brain metastases with EGFR mutation status in lung adenocarcinoma cases and the relationship between ADC histogram analysis differences and overall survival. METHODS:In
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An empirical method for geometric calibration of a photon counting detector-based cone beam CT system J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-07-06 Muhammad Usman Ghani, Andrey Makeev, Joseph A. Manus, Stephen J. Glick, Bahaa Ghammraoui
BACKGROUND:Geometric calibration is essential in developing a reliable computed tomography (CT) system. It involves estimating the geometry under which the angular projections are acquired. Geometric calibration of cone beam CTs employing small area detectors, such as currently available photon counting detectors (PCDs), is challenging when using traditional-based methods due to detectors’ limited
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A fast tomosynthesis method for printed circuit boards based on a multiple multi-resolution reconstruction algorithm J. X-Ray Sci. Technol. (IF 3.0) Pub Date : 2023-07-04 Hui Tang, Tian Li, Yu Bing Lin, Yu Li, Xu Dong Bao
Digital tomosynthesis (DTS) technology has attracted much attention in the field of nondestructive testing of printed circuit boards (PCB) due to its high resolution and suitability to thin slab objects. However, the traditional DTS iterative algorithm is computationally demanding, and its real-time processing of high-resolution and large volume reconstruction is infeasible. To address this issue,