No Abstract

Purpose: The biological effects of ionizing radiation at the cellular and subcellular scales are studied by the number of breaks in the DNA molecule that provides a quantitative description of the stochastic aspects of energy deposition at cellular scales. The Geant4 code represents a suitable theoretical toolkit in microdosimetry and nanodosimetry. In this study, radiation effects due to Auger electrons emitting radionuclides such as   , ,  and are investigated using the Geant4-DNA.

Materials and Methods: The Geant4-DNA is the first Open-access software for the simulation of ionizing radiation and biological damage at the DNA scale. Low-energy electrons, especially Auger electron from Auger electron emitting radionuclides during the slowing-down process, deposit their energy within a nanometer volume.

Results: The average number of Single-Strand Breaks (SSB) and Double-Strand Breaks (DSB) of DNA as a function of energy and distance from the center of the DNA axis are shown.

Conclusion: The highest DSBs yield has occurred at energies less than 1 keV, and  induces a higher DSBs yield.

Purpose: The goal of this study was to evaluate radiographers’ knowledge and practice on different methods for attracting pediatrics’ cooperation in medical imaging departments for achieving high-quality images without repetition and minimum absorbed dose.

Materials and Methods: For conducting this descriptive-analytical study, a researcher-made questionnaire, including two parts of radiographer knowledge and practical methods, which were applied as a routine in the departments for reduction of pediatrics’ stress, was distributed between radiographers.

Results: The results revealed that verbal justification was declared as the most efficient way of informing the parents as compared to the other methods. Establishing verbal communication is the most practical way of engaging the child. Meanwhile, application of immobilization tools, justification of parents by the admission staff, playing music was used, respectively.

Conclusion: Considering these findings, there is a need to equip the imaging department with the appropriate facilities, perform continuous training of radiographers to increase the practice of different techniques and tools.

Purpose: This study focused on accurate quantification of a maximum of Choline-to-Creatine ratio (Max (Cho/Cr)) in 10 Osteosarcoma patients, in comparison with 5 healthy volunteers as our control group using proton Magnetic Resonance Spectroscopy Imaging (1H-MRSI).

Materials and Methods: Max (Cho/Cr) were obtained in 10 patients with Osteosarcoma over their corresponding ratio maps containing diseased tissue, to be compared with Cho/Cr in 5 healthy volunteers at 3T, employing MRSI (Performed Employing Pointed-resolved Spectroscopy (PRESS), TR/TE: 2500s /135 ms) with water-suppression. An extra unsuppressed water Single-Voxel Spectroscopy (SVS) was acquired to provide phase information for further Eddy Current Correction (ECC). Multi-stage preprocessing was applied. Subtract QUEST MRSI as a time-domain technique was employed to accurately quantify the metabolites’ ratios and to estimate the baseline.

Results: An optimal database for Subtract QUEST was achieved based on multiple trials evaluated by acceptable peak-fitting and Cramer-Rao-Bound (CRB). Lipids at frequencies of 0.94 and 1.33ppm were combined to increase the accuracy of the Lipid estimation.

Conclusion: Estimation of Max (Cho/Cr) evaluated over Cho/Cr spatial maps to distinguish Osteosarcoma patients from normal subjects suggested that the proposed quantification method leads to high power and linear classifier with a high degree of reproducibility, considering 1H-MRSI at 3T machine as a high efficacy diagnostic tool for musculoskeletal radiology.

Purpose: Commissioning of a linear accelerator is a process of acquiring a set of data used for patient treatment. This article presents the beam data measurement results from the commissioning of a VitalBeam^TM linac.

Materials and Methods: Dosimetric properties for 6,10, and 15 MV photon beams and 6, 9, 12, and 16 MeV electron beams have been performed. Parameters, including Percentage Depth Dose (PDD), depth dose profile, symmetry, flatness, quality index, output factors, and the vital data for Treatment Planning System (TPS) commissioning were measured. The imported data were checked by CIRS phantom accordingly to IAEA TRS-430, TECDOC. Eight different positions of CIRS phantom CT were planned and treated. Finally, the calculated dose at a determined position was compared with measuring data to TPS validation.

Results: After comparing 84 points in a different plan, the 83 points were in agreement with the criteria, and just for one point in 15 MV failed.

Conclusion: Commissioning of dose and field flatness and symmetry are in tolerance intervals given by Varian. This proves that the studied lines meet the specification and can be used in clinical practice with all available electron and photon energies.

Purpose: The present study was conducted to investigate and classify two groups of healthy children and children with Attention Deficit Hyperactivity Disorder (ADHD) by Effective Connectivity (EC) measure. Since early detection of ADHD can make the treatment process more effective, it is important to diagnose it using new methods.  

Materials and Methods: For this purpose, Effective Connectivity Matrices (ECMs) were constructed based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children of the same age. ECMs of each individual were obtained by the directed Phase Transfer Entropy (dPTE) between each pair of electrodes. ECMs were calculated in five frequency bands including, delta, theta, alpha, beta, and gamma. Based on ECM, an Effective Connectivity Vector (ECV) was constructed as a feature vector for the classification process. Furthermore, ECV of different frequency bands was pooled in one global ECV (gECV). Multilayer Artificial Neural Network (ANN) was used in the steps of classification and feature selection by the Genetic Algorithm (GA).

Results: The highest classification accuracy with the selected features of ECV was related to theta frequency band with 89.7%. After that, the delta frequency band had the highest accuracy with 89.2%. The results of ANN classification and GA on the gECV reported 89.1% of accuracy.

Conclusion: Our findings show that the dPTE measure, which determines effective connectivity between the brain regions, can be used to classify between ADHD and healthy groups. The results of the classification have improved compared to some studies that used the functional connectivity measures.

Purpose: In this study, we retrospectively evaluated chest Computed Tomography (CT) imaging manifestations of the patients with Coronavirus Disease 2019 (COVID-19) to simplify prompt early diagnosis of disease and speed up needed actions for infected patients.

Materials and Methods: Totally, 75 patients who laboratory confirmed COVID-19 pneumonia were enrolled in this study. CT images, demographic and some clinical data of all patients were collected and analyzed retrospectively. Furthermore, for comparison, the patients were divided into two groups as follows: the young and middle-aged group (< 60 years old) and the elderly group (≥ 60 years old).

Results: Based on the evaluation of CT images, 33 patients (44%) showed Ground-Glass Opacity (GGO), 15 patients (20%) showed consolidation, 24 patients (32%) showed mixed GGO and consolidation, 2 patients (2.6%) had bronchial wall thickening, 10 patients (13.3%) had a crazy paving sign, 35 patients (46.6%) had air bronchogram and, 7 patients (9.3%) had cavitation and 2 patients (2.6%) had a tree in the bud. CT images of 3 patients (4%) were normal. In terms of out of lung changes, lymphadenopathy was observed in one patient (1.3%), pleural effusion in 12 patients (16%), and pericardial effusion in 2 patients (2.6%). Lesions were found predominantly in the peripheral (57.3%) and the lower lung region (60%).

Conclusion: CT images of the COVID-19 patients showed various aspects, mainly GGO, consolidation, mixed GGO and consolidation, and air bronchogram. Lesion distribution was predominantly in lower lung region, bilateral and peripheral. Pleural effusion and multiple lobe involvement were significantly higher in the elderly group than that of the young and middle-aged group.

Purpose: Coronavirus disease 2019 (Covid-19), first reported in December 2019 in Wuhan, China, has become a pandemic. Chest imaging is used for the diagnosis of Covid-19 patients and can address problems concerning Reverse Transcription-Polymerase Chain Reaction (RT-PCR) shortcomings. Chest X-ray images can act as an appropriate alternative to Computed Tomography (CT) for diagnosing Covid-19. The purpose of this study is to use a Deep Learning method for diagnosing Covid-19 cases using chest X-ray images. Thus, we propose Covidense based on the pre-trained Densenet-201 model and is trained on a dataset comprising chest X-ray images of Covid-19, normal, bacterial pneumonia, and viral pneumonia cases.

Materials and Methods: In this study, a total number of 1280 chest X-ray images of Covid-19, normal, bacterial and viral pneumonia cases were collected from open access repositories. Covidense, a convolutional neural network model, is based on the pre-trained DenseNet-201 architecture, and after pre-processing the images, it has been trained and tested on the images using the 5-fold cross-validation method.

Results: The accuracy of different classifications including classification of two classes (Covid-19, normal), three classes 1 (Covid-19, normal and bacterial pneumonia), three classes 2 (Covid-19, normal and viral pneumonia), and four classes (Covid-19, normal, bacterial pneumonia and viral pneumonia) are 99.46%, 92.86%, 93.91 %, and 91.01% respectively.

Conclusion: This model can differentiate pneumonia caused by Covid-19 from other types of pneumonia, including bacterial and viral. The proposed model offers high accuracy and can be of great help for effective screening. Thus, reducing the rate of infection spread. Also, it can act as a complementary tool for the detection and diagnosis of Covid-19.

Magnetic particle imaging was introduced in 2005 as a new tomographic medical imaging modality and is still under development. Magnetic particle imaging determines the spatial distribution of magnetic nanoparticles by their interaction with an external excitation magnetic field. Therefore, there is no ionizing radiation dose in this trace-based modality. Magnetic nanoparticle imaging provides characteristics, including high spatial and temporal resolution, high sensitivity, expected from an ideal imaging method, and it is also an inherently quantitative method.

In this paper, the properties of magnetic fields and nanoparticles used in Magnetic particle imaging, as well as its applications are discussed.

Purpose: Using an itra-operative gamma probe after injection of radiotracer during surgery helps the surgeon to identify the sentinel lymph node of regional metastasis through the detection of radiation. This work reports the design and specification of an integrated gamma probe (GammaPen), developed by our company.

Materials and Methods: GammaPen is a compact and fully integrated gamma probe. The detector module consists of a thallium-activated Cesium Iodide (CsI (Tl)) scintillator, and a Silicon Photo Multiplier (SiPM), shielded using Tungsten housing. Probe sensitivity, spatial resolution and angular resolution in air and water, and side and back shielding effectiveness were measured to evaluate the performance of the probe based on NEMA NU3 standard.

Results: The sensitivity of the probe in the air/water at distances of 10, 30, and 50 mm is 18784/176800, 3500/3050, and 1575/1104 cps/MBq. The spatial and angular resolutions in the air/scattering medium are 40/47 mm and 77/87 degrees at a 30 mm distance from the probe. The detector shielding effectiveness and leakage sensitivity are 99.91% and 0.09%, respectively.

Conclusion: The results and surgeon experience in the operating room showed that GammaPen can be effectively used for sentinel lymph node localization.