Purpose: Brain-Computer Interface (BCI) provides a secondary communication pathway for patients with neuromuscular diseases such as amyotrophic lateral sclerosis (ALS) or brainstem stroke in which they are almost incapacitated to move or talk. BCI enacts neural oscillations to generate a command signal for machines to operate desired tasks instead of patients. Steady-State Visual Evoked Potential (SSVEP) is the brain response to a visual stimulus, with the same frequency as its eliciting signal (or its harmonics), that has been widely used in BCI environments. In order to provide a more convenient situation for BCI users, we aim to find the best single-channel EEG, which results in the highest accuracy for detecting SSVEP.

Materials and Methods: We developed a Deep Convolutional Neural Network with single-channel EEG as input to classify a 40-class SSVEP; each class represents a stimulus, which has been acquired from 35 subjects. We used 3.5 s windows of the data (Trials of 3.5 seconds length for each class) to train our model and leave-one-subject-out cross-validation for the testing.

Results: The proposed method resulted in the average classification accuracy of 74.30%±20.85 and Information Transfer Rate (ITR) of 57.51 bpm which outperforms the previous single-channel SSVEP BCIs in terms of ITR. Also, the O1 channel achieved the best performance criteria among the channels in the occipital and parietal lobes, which seems reasonable according to previous researches for finding the location of neurons, responsible for visual tasks in the brain.

Conclusion: In this study, we dedicated our efforts to reduce the number of EEG channels to a single channel while proposing a deep learning strategy for an SSVEP-based BCI speller to make it more feasible for patients whose lives are dependent on such systems. The overall results, although not ideal, open a new promising window toward a feasible BCI system.

Purpose: Inhibitory and excitatory neurons play an essential role in brain function, and we aim to introduce an automatic method to discriminate these two populations based on features of the shape of their spikes. Consequently, we will explain the spike extraction from raw data of a single shank electrode and determine the best features of spike waveforms for the classification of neurons. It is noteworthy that, to the best of our knowledge, classification of inhibitory and excitatory neurons using the shape features extracted from their spike waveforms has not been done before.

Materials and Methods: In this paper, we use a dataset of mouse hippocampus neurons in which the neuron types (inhibitory or excitatory) have been verified optogenetically. For the classification of mouse hippocampus neurons, we extracted eight shape features of their spike waveforms in addition to their firing rates and used three types of classifiers: K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) to analyze the discriminatory power of features based on the accuracy of the classifications.

Results: We showed that Spike asymmetry, Peak-to-trough ratio, Recovery slope, and Duration between peaks were four shape features of spike waveforms participated in the optimum feature subsets that resulted in maximum classification accuracy. Moreover, the SVM classifier with RBF kernel resulted in maximum accuracy of %96.91 ± %13.03 and was identified as the best classifier.

Conclusion: In this study, we found that shape features of spike waveforms can accurately classify inhibitory and excitatory neurons of mouse hippocampus. Also, we found an optimum subset of shape features of spike waveforms that resulted in better classification performance than previously proposed subsets of features used for clustering of neurons. Our findings open a promising way toward a functional classification of neurons automatically.

Purpose: The current study aimed to estimate photon skyshine dose rate from a Varian linac equippedwith a Flattening Filter (FF) and its FF-Free (FFF) mode. The skyshine photons from a Linac bunker can influence the radiation dose received by personnel and the public in radiation therapy centers.

Materials and Methods: In the current study skyshine dose from the conventional flattened beam and the flattening-free beam were compared. The MCNPX Monte Carlo code was used to model an18 MeV photon beam of Varian linac. The skyshine radiation was calculated for FF and FFF linac photon beams at the control room, parking, sidewalk, and corridor around the linac room.

Results: For the conventional beam, the skyshine dose rates of 0.53, 0.42, 0.45, and 0.50 mSv/h were estimated for the control room, corridor, sidewalk, and parking, respectively. While for the FFF beam, dose rates of 0.21, 0.20, 0.20, and 0.23 mSv/h were estimated for the same positions, respectively. The results indicated that the empirical method of NCRP 151 can not distinguish between FF and FFF beams in skyshine dose calculations. Our results found a 50% lower level dose rate from the FFF beam at distant and nearby locations.

Conclusion: The findings of current can be helpful in the radiation dose calculations and the radiation protection designation of radiation therapy bunkers. 

Introduction: Generally, the benefits of radiological examinations performed on individuals far outweigh their risks; however, this is not true when the radiographic system fails to work properly. Therefore, to avoid such errors, it is crucial to frequently perform Quality Control (QC) checks in an imaging facility.

Material and Methods: A total of 11 highly-referred centers out of 62 radiology rooms located in Yazd province were included in this investigation, and QC tests comprising light/radiation field alignment, the accuracy of kilovoltage and exposure time, reproducibility of kilovoltage, exposure time, and output, and linearity of output against exposure time and milliamperage were performed for each equipment. The light and radiation field alignment test were carried out by a quantitative assessment of digital images of a collimator template (PTW-Freiburg, Germany). The measurements were made by a Barracuda package and a Multi-Purpose Detector (MPD).

Results: In terms of the light/radiation field alignment check, unit A failed to satisfy the national regulations. Concerning the timer reproducibility, 64% of the units failed to meet the criteria. All of the devices passed the rest of the checks satisfactorily.

Conclusion: This study uncovered that most of the radiology rooms in Yazd province are in an adequate situation based on the QC tests; however, more than half of the units do not satisfy the timer reproducibility criteria. Hence, more supervision needs to be directed at these systems by qualified radiation safety officers who are responsible for the protection of the population against ionization radiation.

Purpose: Sleep apnea is a common disease among women, and mainly men. The most dangerous complication of this disorder is heart stroke. Other complications include insufficient sleep and resulting daytime tiredness and illness that affect the individual's activities during the day, disrupt their life. Therefore, identifying this disease is important.

Materials and Methods: We used Electroencephalogram (EEG) and Electrocardiogram (ECG) channels from the data of 25 patients with sleep apnea, for each type of sleep apnea, 8 nonlinear-like features, including fractal dimension, correlation dimension, certainty, recurrence rate, mean diagonal lines, the entropy of recursive quantification analysis, sample Entropy, and Shannon entropy were extracted. Then, feature matrices were sorted using principal component analysis in the order of linear combination of features, and the 20 selected features were chosen, normalized using common methods, and fed to different classifiers. Two 5-class and 2-class classification methods were assessed. In the 5-classification, three classifiers were used; the support vector machine, k-nearest neighbor, and multilayer perceptron.

Results: The results showed that the highest mean validity, accuracy, sensitivity, and specificity for the SVM classifier was 88.45%, 88.35%, 88.33%, and 88.32%, respectively. In the 2-class approach, in addition to the mentioned classifiers, linear discriminant analysis, Bayes, and majority voting were used, and each class was considered against all classes. The highest average validity, average accuracy, average sensitivity, average specificity using the majority rule voting was 94.35%, 94.30%, 94.32%, and 94.15% respectively.

Conclusion: When the results of classifiers are combined with the majority voting method, the validity of identifying the classes increases. The average validity for this method was obtained at 94.42%, which was higher than several other studies. It is recommended that databases with a larger sample size be used. This would lead to increased reliability of the proposed analysis method. Moreover, using novel deep-learning-based methods could help obtain better results.

Purpose: Recently, the application of high atomic number nanoparticles is suggested in the field of radiotherapy to improve physical dose enhancement and hence treatment efficiency. Several factors such as concentration and material of nanoparticles and energy of beam define the amount of dose enhancement in the target in the presence of nanoparticles.

Materials and Methods: In this approach, a spherical cell was simulated through the Geant4 Monte Carlo toolkit which contained a nucleus and nanoparticles distributed through the cell. To investigate the effect of the concentration of nanoparticles on the deposited dose, it ranged from 3 mg/g to 30 mg/g for different materials like gold, silver, gadolinium, and platinum. Also, various mono-energetic photon beams included low and high energy sources were applied.

Results: The results proved that as the concentration increased, the Dose Enhancement Factor (DEF) enlarged. Overall, almost for all energy and material that were used in this study, the maximum of DEF values occurred in the concentration of 30 mg/g. Moreover, lower energy sources presented higher DEF compared to other sources. The results indicated that the highest amount of DEF transpired for 35 keV photon beams equal to 14.67. Also, the K-edge energy of each material affects DEF values.

Conclusion: To obtain a better outcome in the use of nanoparticles in combination with radiotherapy, a higher concentration of nanoparticles and low-energy photons should be considered to optimize the DEF and thus the treatment ratio.

Purpose: Glioblastoma Multiform (GBM) is one of the most common and deadly malignant brain tumors. Surgery is the primary treatment, and careful surgery can minimize recurrence odds. Magnetic Resonance Imaging (MRI) imaging with Magnetic Resonance Spectroscopy (MRS) is used to diagnose various types of tumors in the Central Nervous System (CNS). In this study, several classification methods were used to separate tumor and healthy tissue.
Materials and Methods: This study examined the MRI and MRS results of seven people enrolled in this study in 2018. The data was obtained with a prescription from a neurologist and neurosurgeon. Choline (Cho) and N-Acetylaspartate (NAA) metabolite signals were selected as the reference signal after preprocessing and removing the water signal. With the support of 3 radiologists, each tumor and healthy vesicles were identified for every patient. Then, tumor and healthy voxels were separated based on Multilayer Perceptron (MLP), linear Support Vector Machine (SVM), Gaussian SVM, and Fuzzy system using the obtained values and four different methods.
Results: Data extracted from Cho and NAA metabolites were fed into MLP, linear SVM, Gaussian and Fuzzy SVM as input, and the amounts of accuracy, sensitivity, and specificity were determined for each method. The maximum accuracy for training mode and test mode was equal to 89.7% and 87%, respectively, specific to classification using Gaussian SVM. The results also showed that the classification accuracy can be significantly increased by increasing the number of fuzzy membership functions from 2 to 6.
Conclusion: The results of this study suggested that a more complex classification system, such as SVM with a Gaussian kernel and fuzzy system can be more efficient and reliable when it comes to separating tumor tissue from healthy tissues from MRS data.

Purpose: Breast cancer is the most common malignancy among women which in some cases is followed by breast reconstructions. The objective of the experimental study is to investigate the effect of the silicone prosthesis implementation on the dose distribution of radiotherapy.

Materials and Methods: Initially CT images of 7 mastectomy breast patients with silicone prosthesis were imported to the Monaco treatment planning system. A treatment plan consisting of two tangential photon fields with a prescription dose of 50Gy was arranged. To study the effect and water equivalency of silicone prosthesis, dose distribution of treatment plan was acquired in two conditions: 1) considering the real electron density of silicone prosthesis; 2) modifying (Relative electron density) RED of silicone prosthesis to 1 to virtually assume it as soft tissue (water). The results were then compared by VeriSoft software to evaluate the gamma index.

Results: The obtained results indicated that the RED for the silicon prosthesis varies between 0.7 and 1.14 while the RED for soft tissue is approximately 1. Also, the Dose-volume histogram curves for both conditions indicated that the minimum and maximum differences ranged from 1% to 4%. The significant differences might be due to the presence of the air cavity or bubbles in the silicone prosthesis implementation or air voxels between prostheses and soft tissue.

Conclusion: The obtained results showed that if there is no air cavity in silicone prosthesis and the surgery is performed in a way that no volume of air is left between the prosthesis and breast tissue, the effect and presence of silicone prosthesis will be similar to soft tissue (water).

Purpose: Humans are always exposed to ionizing radiation from their environment, which can have destructive effects. This study aimed to measure background gamma radiation and estimate annual effective dose and excess cancer risk in Gonabad city.

Materials and Methods: The dose rate due to indoor and outdoor background radiation was measured by RDS-30 radiation survey meter at five zones on the map, including North, South, East, West, and center. Then, the annual effective dose and excess lifetime cancer risk were calculated by associated equations.

Results: Mean dose rates for outdoor and indoor spaces were 0.111 µSv/h and 0.139 µSv/h, respectively. The mean background dose rate of indoor space was significantly higher than that of outdoor space. Annual effective dose and excess lifetime cancer risk were obtained as 0.817 and 2.85×10^-3, respectively.

Conclusion: Background radiation dose, annual effective dose, and cancer risk for Gonabad city were higher than global ones. Further investigations are needed to encompass internal background radiation doses in annual effective dose.

A variety of imaging modalities include X-ray-based Computed Tomography (CT) scan, Ultrasound (US), Magnetic Resonance Imaging (MRI), Nuclear Medical Imaging (NMI), and Optical Imaging (OI) are used to help diagnose and treat diseases through anatomical, physiological, and functional representation. With the advent of molecular imaging using nanoparticles, detailed information about properties is provided and facilitates early detection of malignancies. Now, novel approaches in nanoparticle designing, the development of hybrid imaging modalities, and improvements in the sensitivity of instruments have raised the level of disease diagnosis.

Regarding the fact that the molecular imaging fundamentals and basis of materials as contrast agents are different from each other, we have updated the brief synopsis of basic principles of imaging technique containing important points in detail with a practical approach.