No Abstract No Abstract No Abstract

Purpose: Olfactory system is a vital sensory system in mammals, giving them the ability to connect with their environment. Anosmia, or the complete loss of olfaction ability, which could be caused by injuries, is an interesting topic for inspectors with the aim of diagnosing patients. Sniffing test is currently utilized to examine if an individual is suffering from anosmia; however, functional Magnetic Resonance Imaging (fMRI) provides unique information about the structure and function of the different areas of the human brain, and therefore this noninvasive method could be used as a tool to locate the olfactory-related regions of the brain.

Materials and Methods: In this study, by recruiting 31 healthy and anosmic individuals, we investigated the neural Blood Oxygenation Level Dependent (BOLD) responses in the olfactory cortices following two odor stimuli, rose and eucalyptus, by using a 3T MR scanner.

Results: Comparing the two groups, we observed a network of brain areas being more active in normal individuals when smelling the odors. In addition, a number of brain areas also showed an activation decline during the odor stimuli, which is hypothesized as a resource allocation deactivation.

Conclusion: This study illustrated alterations in the brain activity between normal individuals and anosmic patients when smelling odors, and could potentially help for a better anosmia diagnosis in the future.

Purpose: The purpose of this study was to evaluate the risk of gonad cancer induction attributable to pelvic radiation therapy in adult patients. 

Methods: By characterizing the peripheral dose the TLDs were placed on the testis and ovary in two fractions of radiotherapy. All patients delivered a 45 Gy total dose in 4 fields in the prone position with 3D planning. The doses from a linear accelerator at 18 MV photon beam, were investigated.

Results: The mean excess relative risk (ERR) based on the BEIR IIV models of men and women after 5 and 10 year of radiotherapy treatment for pelvic radiotherapy was 0.825±0.168, 0.948±0.504, 0.700±0.135, and 0.803±0.407 respectively.

Conclusion: Estimating the second cancer risk of untargeted organs is crucial in radiotherapy. Using the single-energy mode linear accelerator and proper shields can be minimized the out-of-field doses.

Purpose: Sleep is a subconscious state, and the brain is active during it. Automatic classification of sleep stages can help identify various diseases. In recent years, automatic sleep monitoring using deep learning networks has attracted the attention of researchers.

Materials and Methods: In this paper, a deep learning type neural network called Stacked Autoencoders (SAEs) is used for automatically classifying sleep stages. SAEs are a kind of neural network with encoder and decoder blocks. The function of these networks is similar to the human brain and is capable of automatically processing signals; also SAEs are robust to noise. To prove the efficiency of this network, in addition to examining the effect of various biological signals such as Electrocardiogram (ECG) and Electroencephalogram (EEG) on the performance of sleep stage classification, Sleep Heart Health Study (SHHS) and ISRUC standard databases have been used, which include night recordings of 30 and 10 healthy humans, respectively.  

Results: The accuracy of classifying 2 to 6 classes by SHHS database are 0.995, 0.983, 0.9780, 0.9688, 0.961, and on ISRUC database accuracies are 0.996, 0.994, 0.9511, and 0.9431. Moreover, the proposed network can classify wake, deep sleep, and light sleep using the ECG signal (acc = 0.75, kappa = 0.69).

Conclusion: In the review of the results, it is concluded that sleep stages classification based on EEG signal has better results, still acquisition of ECG signal and its acceptable results can be a good alternative to use. In addition to its high ability of the proposed method to detect sleep stages, this network is robust to noise, which is very necessary and important for the clinical processing of sleep signals.

Purpose: The mortality rate of fetuses due to heart defects is a major concern for clinicians. The fetus's heart is monitored non-invasively using the abdominal Electrocardiogram (ECG) of the mother. Most of the methods in literature diagnose fetal arrhythmia based on fetal heart rate. However, there are various challenges in fetal heart rate monitoring and arrhythmia detection. Therefore, very few methods are explored for fetal arrhythmia classification and have not achieved promising results.

Materials and Methods: In this article, a fetal arrhythmia classification method is investigated. The method has exploited the transfer learning principle where DenseNet architecture is utilized to learn fetal ECG patterns. Fetal ECG (fECG) signal extracted from the mothers abdominal has been processed for denoising and heartbeats are segmented using signal processing techniques. The extracted heartbeats have transformed into 2D fECG images to re-train the pre-trained DenseNet architecture.

Results: The proposed method has been evaluated on the publicly available Non-Invasive Fetal Arrhythmia Database (NIFADB) of Physionet and achieved 98.56% classification accuracy, thus outperforming other existing methods.

Conclusion: The arrhythmia in a fetus can be detected using a non-invasive fetal ECG. Due to the faster convergence of the learning algorithm, the proposed method offers better fetal diagnosis in real-time.

Purpose: This research aimed to estimate the Mass Attenuation Coefficient (MAC) for the various nanoparticles in diagnostic imaging in order to assess and compare the changes in a bulk state.

Materials and Methods: To Using Monte Carlo N-Particle eXtended (MCNPX) code, nanoparticles were simulated in the target in order to compute the MAC considering the target. The Materials, including Bi, Pb NPs, Pb, W NPs, W, PbO NPs, Bi NPs, Bi₂O₃ NPs, and WO₃ NPs were used in the present study. The gathered data were compared with the theoretical results of the XCOM software for validation.

Results: The findings demonstrated that the radioprotective characteristics of nanoparticles in comparison to the bulk materials were better. Among all these nanoparticles, the rate of attenuation of tungsten nanoparticles was higher than that of other nanoparticles. On the other hand, the density and attenuation rate of nanoparticles of PbO, Bi₂O₃ and WO₃ were lower than those of nanoparticles Pb, W, and Bi. Therefore, all of the abovementioned nanoparticles were lightweight and their design was more flexible than that of bulk materials.

Conclusion: It was concluded that the use of nanoparticles in the protective materials considerably increased the radioprotective characteristics in the diagnostic radiography energy range.

Purpose: Gamma cameras are one of the most promising technologies for in-vivo range monitoring in proton therapy. Monte Carlo (MC) simulation is a common calculation-based technique to design and optimize gamma cameras. However, it is prohibitively time-consuming. Analytical modeling speeds up the process of finding the optimal design.

Materials and Methods: We proposed an analytical method using the efficiency-resolution trade-off for optimizing a knife-edge collimator based on the range retrieval precision of protons. Monte Carlo simulation was used for validation of obtained collimator efficiencies.

Results: The model predicts that for the optimal range retrieval precision, the ratio of the source-to-detector distance  to the source-to-collimator distance  should be ranging from . For a special case, it was found that assuming an ideal detector , the falloff retrieval precision is optimal at  independent of the collimator resolution. Moreover, using the optimized camera, the difference between the MC calculated range and the absolute range was 0.5 cm (the relative error is about 3%).

Conclusion: It was found that the collimator parameters are in good agreement in comparison with that of the MC results reported in the literature. The analytical method studied in this work can be used to design and optimize imaging systems based on KE collimators in combination with new detectors in a fast and reliable way.

Purpose: The danger of radiation at low doses continues linearly, and without a threshold, investigations concluded that although the risk of cancer from Computed Tomography (CT) scans is low, it is not zero.

This study aims to determine the patient's radiation dose and estimate the Lifetime Attributable Risk (LAR) of cancer incidence for a single chest CT scan in children.

Materials and Methods: We divided 1,105 children into four age groups: 0 years, 5 years, 10 years, and 15 years. Dosimetric data of chest CT scan were plugged in VirtualDoseCT software, and organ dose and effective dose were calculated. The cancer risk based on organ dose is estimated according to the BEIR VII report.

Results: The highest dose in boys was related to lung (5.13 - 6.8 mSv) and heart (5.27-5.97 mSv), and in girls, lung (4.98 - 5.91 mSv), breast (4.24 - 5.21 mSv), and heart (4.9 - 5.71 mSv) had the highest dose. The highest LAR (per 100,000) was obtained for the breast in the age group of 0 years (61.01), followed by the breast for the age group of 5 years (46.16) and lung in the age group of 0 years (43.32) in girls.

Conclusion: This study shows a better concept of radiation dose in the chest CT scan in children and how much effective dose and organ dose values increase the cancer risk.

Purpose: A new code based on Helmholtz decomposition is presented to separate longitudinal (pressure) and transverse (shear) components of a mixed wave field. This algorithm will help isolate shear or pressure components of an elastic wave to further concentrate on each specific wave and its physical characteristics, particularly in medical imaging instrument development and image processing techniques.

Materials and Methods: Using the combination of Fourier transform and Helmholtz decomposition, first, the mathematical basis of the work is prepared. After reaching a usable formula, this basis is embedded in the Code written in MATLAB program. Then, various test data containing shear and pressure waves were created and fed to the Code to evaluate its ability to decompose the displacements into the shear and pressure waves.

Results: This new algorithm successfully isolated the transverse and longitudinal wavefront of the mixed wavefield. The Code demonstrated 100% accuracy for separating the shear wave and more than 99% for the pressure wave. Moreover, the background noise was kept under 0.03% in every step.

Conclusion: The results show that using Helmholtz decomposition in Fourier space on 3D data can help decompose a displacement field into its irrotational and solenoidal components with high accuracy. A weak dependency on wave thickness and contrast was observed, but the algorithm's accuracy never fell below 99%.

Purpose: Human Immunodeficiency Virus (HIV) continues to be a disease that kills thousands of individuals each year. The HIV infection is incurable. However, HIV infection has turned into a treatable chronic health condition because of improved access to efficient HIV prevention, diagnosis, treatment, and care. Transmission Electron Microscopy’s (TEM) ability to directly visualize virus particles and distinguish ultrastructure morphology at the nanometer scale, makes it useful in HIV-1 research where it is used for assessing the actions of inhibitors that obstruct the maturation and morphogenesis phases of the virus lifecycle. Hence with its use, the disease's serious stage can be avoided by receiving an early diagnosis.

Materials and Methods: Through the dedicated use of computer vision frameworks and machine learning techniques, we have developed an optimized low-computational-cost 8-layer Convolutional Neural Network (CNN) backbone capable of classifying HIV-1 virions at various stages of maturity and morphogenesis. The dataset including TEM images of HIV-1 viral life cycle phases is analysed and augmented through various techniques to make the framework robust in real-time. The CNN layers then extract pertinent disease traits from TEM images and utilise them to provide diagnostic predictions.

Results: It was discovered that the framework performed with an accuracy of 99.76% on the training set, 85.83% on the validation set, and 91.33% on the test set, after being trained on a wide range of micrographs which comprised of different experimental samples and magnifications.

Conclusion: The suggested network's performance was compared to that of other state-of-the-art networks, and it was discovered that the proposed model was undisputed for classifying TEM images of unseen HIV-1 virion and required less time to train and tweak its weights. The framework can operate more effectively than machine learning algorithms that consume a lot of resources and can be deployed with limited computation and memory resource requirements.

Propose: shielding medical radiation requires deep knowledge of radiation physics and shielding design methods. Materials and Methods: Effective neutron mass removal cross-section (Σ_R/ρ) was derived for the ordinary concrete doped with 50nm of TiO₂ (5%), Sm₂O₃ (5%), WO₃(5%), B₄H (5%), SiO₂ (5%) nanoparticles mixture with MCNP5 estimation and N-XCOM calculation was conducted. The ordinary concrete (with a density of 2.35 g/cm³) composition elements Σ_R/ρ were also estimated. An 18MV Variuan 2100Clinac room made of obtained nano-concrete and with three legs included maze simulated and secondary neutron and capture γ-ray dose equivalent (DE) were estimated. required shielding Lead and Borated-Polyethylene (BPE) were calculated according to the estimated doses and guidelines a negligible shielding, close to an Open-Door maze was obtained. Results: Total Σ_R/ρ of the neutron with energies 100 keV-2000keV was estimated as 0.02802-0.02687 cm2/g using the MC simulation method in good geometry while N-XCOM software calculated the same values as 0.02810- 0.02687 cm2/g. Σ_R/ρ was also derived using MC and N-XCOM for the elements in the pure ordinary concrete. MCNP5/1.60 MC simulation code was calculated secondary neutron and capture γ-ray dose equivalent as 1.65×10-07 mSv/isocenter Gy and 7.98×10-06 mSv/isocenter Gy.

Conclusion: It was concluded that the nanoparticles mixture present in the ordinary concrete enhanced the shielding properties of the concrete and additional bendings in the maze besides the effect of the nanoparticles, designed a room with negligible shielding at the maze entrance that was close to the Open-Door 18MV room.

Autonomous Sensory Meridian Response is a novel phenomenon that is very popular these days on Youtube and Reddit to its anti-anxiety effects. As the name suggests, ASMR is a relaxing warm sensation that begins on the scalp and spreads throughout the body. This technique is also known as "brain massage," and it relies on soothing sights and sounds, like whispers and slow movements.One of the most substantial reasons for investigating these videos is to find out their scientific roots, which can be from different approaches. In this paper, we intended to examine the physiological changes such as Heart Rate (HR) as well as Galvanic Skin Conductance (GSC) levels before and after watching a single session ASMR video. The dependent t-test statistical analysis by SPSS results with P-value <=0.01 indicated that after a single session of ASMR watching, the heart rate decreased significantly comparing the baseline data. In addition, the skin conductance was slightly reduced as well, but not significantly. These physiological findings prove that ASMR could be an affordable, portable, and immediate anxiety relief for those struggling with anxiety-based disorders, especially for patients who do not respond well to medication or seek alternatives to anti-anxiety medications due to the wide range of side effects or would like to try it for better results along with the prescribed drugs.

Purpose: Considering the high prevalence of breast cancer and the radiation sensitivity of breast tissue, it is necessary to optimize the treatment process of this tumor, especially when using radiation therapy methods.

The present study was conducted to investigate the effect and complications of new anti-estrogens on the effectiveness of breast cancer treatment.

Materials and Methods: Articles were searched in PubMed, Science direct, Embassy, Cochran, and Scopus databases using the keywords Cancer AND Anti-estrogen, Breast Cancer AND anti-estrogen AND mice, Breast cancer And anti-estrogen AND rat. The authors reviewed the abstract and full text of the articles and the relevant studies were selected for systematic review.

Results: The anti-estrogens used in the reviewed studies included TAM, RAL, SS1020, SS1010, GW5638, OSP, 4-OHTAM, and TOR. Anti-estrogen-related side effects included liver and uterine complications, especially in the case of using TAM anti-estrogen (54%). Moreover, uterine hypertrophy was observed using GW5638, RAL, and SS1010 anti-estrogens; while it happened with a lower percentage than TAM, 16%, 14%, and 13%, respectively. Side effects were significantly reduced by reducing the prescribed dose. So that this reduction for TAM is from 54% to 33%. In relation to the effect of antiestrogens on tumor treatment, the most effective and least complications were related to the antiestrogen "SS1020".

Conclusion: Based on the results of reviewed studies, SS1020, which has no estrogenic and genotoxic activity, was safe and the most effective anti-estrogen against breast cancer in animals and also in humans.

Purpose: Radiotherapy (RT), which is considered one of the critical treatments for cancer patients is also known as adjuvant therapy and palliative care, and can be attempted alone or concurrent with chemotherapy. Although RT reduces the risk of recurrence, the scattered dose may enhance the risk of secondary cancer induction; this is raising some challenges in clinical practice. To the best of our knowledge, few studies to date have assessed such effects of brain cancer adjuvant radiotherapy.

Materials and Methods: We estimated the RT-induced risk of secondary cancer for a 45-year-old patient who had undergone radiotherapy of the head and pelvis with a 6 MV photon beam in 15 and 10 sessions, respectively. The absorbed dose by the thyroid, breast, eye lenses, region overlying ovaries, and parotids was measured using Thermoluminescent Dosimeters (TLD). Since the patient was scanned before radiotherapy, it was decided to calculate their risk as well. To evaluate the cancer risk, radiobiological models for Excess Absolute Risk (EAR), as well as Excess Relative Risk (ERR) published by the Committee on the Biological Effects of Ionizing Radiation (BEIR) in report VII, were implemented. This study thus aimed to estimate the Risk of Exposure-Induced Death (REID) and assess the radiation dose delivered to patients from Computed Tomography (CT) scans and common diagnostic nuclear medicine examinations.

Results: The mean risk of secondary cancer for sensitive organs was calculated 3 years after radiotherapy. The highest estimated ERR was related to the region overlying right and left ovaries for pelvic radiotherapy (47.82) and (51.17), and the next highest EAR followed by right and left eye lenses for brain radiotherapy (18.09) and (15.43), respectively. In addition, other cancers arising from CT scans had the highest REID values for solid cancer (0.0015) and bone scans revealed the highest REID values for other cancers (0.00121).

Conclusion: Calculating the corresponding risks of RT is of great significance for the patients in procedural change. Choosing proper field sizes and adapted techniques to avoid excessive doses to healthy organs can thus be a great assistance in this regard.