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

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

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

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-

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

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)

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

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

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

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

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

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

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

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:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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%)

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,

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

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

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:

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

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

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

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

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

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

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

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

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

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

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)

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°

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

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,

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

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

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

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