Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 11 2 2024 03 31 158 161 Mahdie Izadpanah.K Institute of Artificial Intelligence, Shaoxing University,312000 Shaoxing, 508 West Huancheng Road, Yuecheng District, Zhejiang Province, China Ahmad Jalili Department of Computer Engineering, Faculty of Basic Sciences and Engineering, Gonbad Kavous University, Gonbad Kavous, Iran Mustafa Ghaderzadeh School of Nursing and Health Sciences of Boukan, Urmia University of Medical Sciences, Urmia, Iran Mehdi Gheisari Institute of Artificial Intelligence, Shaoxing University,312000 Shaoxing, 508 West Huancheng Road, Yuecheng District, Zhejiang Province, China 2024 02 29 Over the last decade, there has been a notable and rapid advancement in artificial intelligence (AI), reflecting substantial progress in sophistication and problem-solving capabilities. This evolution has extended across various sectors, encompassing manufacturing, transportation, finance, education, and healthcare. Particularly noteworthy is AI's potential to drive progress in nuclear applications, science, and technology, raising ethical and legal considerations. This academic paper, titled "AI in Nuclear Medical Applications: Challenges and Opportunities," undertakes a thorough exploration of the intricate relationship between AI and nuclear technology. Moving beyond a simple acknowledgment of AI's current capabilities, the paper delves into the nuanced landscape of challenges and opportunities within the realm of nuclear medical applications. It meticulously examines the ethical and legal dimensions inherent in this symbiotic relationship, emphasizing responsible and accountable utilization of AI in the nuclear domain. The research focal point is the strategic deployment of AI capabilities in nuclear medicine, highlighting potential positive contributions to address contemporary challenges. From optimizing medical imaging methodologies to facilitating disease theranostics, the paper critically evaluates the transformative impact of AI on nuclear medical applications. By elucidating specific areas where AI has already demonstrated improvements, the research aims to provide a comprehensive understanding of the current landscape. Keywords include Artificial Intelligence, Radiomics, Radiotherapy, Medical Imaging, Radiopharmacy, and Disease Theranostics. https://fbt.tums.ac.ir/index.php/fbt/article/view/955 https://fbt.tums.ac.ir/index.php/fbt/article/download/955/389

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 11 2 2023 09 30 169 176 Akram Shahidani Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University Manijhe Mokhtari Dizaji . Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University Zeinab Shankayi School of Medicine, Baqiyatallah University, Tehran, Iran Mahmoud Najafi Faculty of Mathematical Sciences, Kent State University, Ohio, USA 2022 10 13 2023 01 02 Purpose: Magnetic Resonance Imaging (MRI) can guide the surgical strategy to identify brain tumors and monitor treatment response. It is possible to use transcranial Ultrasound (US) for periodical follow-ups. Ultrasound waves pass through the delicate areas of the skull called acoustic windows. In this study, the efficiency of ultrasound imaging was performed to diagnose glioblastoma brain tumors and the results were compared with MR images. Materials and Methods: Male Wistar rats were anesthetized by intraperitoneal injection of Ketamine and Xylazine. A stereotaxic device was used to determine the injection coordinates. C6 GBM cell lines were injected into the brains of rats. After two weeks, the formation of a glioblastoma tumor was confirmed histopathologically. The brain of animals was imaged by B-mode ultrasound and MRI. The section with the largest tumor dimensions was selected and the dimensions of the skull and tumor were measured based on the pixel size of each of the imaging methods. Pearson coefficient of correlation and Limits Of Agreement (LOA) were calculated for comparisons of the skull and tumor dimensions. Results: The skull and the tumor dimensions showed a significant correlation between the B-mode ultrasound and the MRI measurements (R=0.99 and p<0.05). According to the Bland-Altman analysis, the mean difference was 0.31 mm (SD=0.20) for skull and tumor dimensions. The exact shape of the tumor is not completely clear in the ultrasound images, but it can be useful to detect the presence of the tumor and its approximate dimensions. Conclusion: In conclusion, a glioblastoma tumor was produced in the male Wistar rat. The tumor dimensions were properly assessed by B-mode ultrasound image processing and compared with MR imaging. https://fbt.tums.ac.ir/index.php/fbt/article/view/564 https://fbt.tums.ac.ir/index.php/fbt/article/download/564/357

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 11 2 2024 03 31 296 301 Mohammad Amin Younesi Heravi Department of Medical Physics and Radiology, Faculty of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran Mohammad Keshtkar Department of Medical Physics and Radiology, Faculty of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran Emad Khoshdel Student Research Committee, North Khorasan University of Medical Sciences, Bojnurd, Iran Salar Poorbarat Student Research Committee, Faculty of Nursing, North Khorasan University of Medical Sciences, Bojnurd, Iran Morteza Pishghadam Department of Medical Physics and Radiology, Faculty of Medicine, North Khorasan University of Medical Sciences, Bojnurd, Iran Mahsa Jafarzadeh Hesari North Khorasan University of Medical Sciences, Bojnurd, Iran 2022 12 24 2023 02 15 Purpose: Although ionizing radiation is useful in diagnosing various diseases, it can cause potential biological damage such as cancer, cataracts, and fetal damage for patients and staff working in radiology departments. Therefore, awareness and practice about the application of radiation protection are essential. This research aims to investigate radiology personnel's knowledge, attitude, and performance in the north and northeast of Iran regarding radiation protection. Methods: This descriptive-analytical cross-sectional study was conducted using a 30-question questionnaire among 435 radiology personnel in North Khorasan, Razavi Khorasan, Golestan, and Mazandaran provinces. This questionnaire included questions related to demographic information and the level of knowledge, attitude, and performance of radiology personnel regarding radiation protection. Data analysis was also analyzed using SPSS-19 software. Result: The participation rate of radiology personnel was 80.55%, and the mean and standard deviation of their knowledge, attitude, and performance regarding radiation protection were 45.9907±1.294, 78.1531±4.707, and 44.9368±6.88, respectively. Based on the results of the study, there is no significant relationship between gender and knowledge, attitude and performance of personnel (P=0.781, P=0.156, and P=0.87), but between education degree and attitude of personnel, between working years and expertise of personnel, and also between job title and philosophy, a significant relationship was observed between personnel (P=0.026, P=0.019, and P=0.003, respectively) Conclusion: Based on the results of this study, it is suggested that employees with fewer years of work be encouraged to participate in radiation protection courses and workshops. It is also better to periodically consider training programs on radiation protection in in-service training for personnel. https://fbt.tums.ac.ir/index.php/fbt/article/view/622 https://fbt.tums.ac.ir/index.php/fbt/article/download/622/387

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 11 2 2024 03 31 286 295 EN Arunkumar Ram Department of Biomedical Engineering, MGM’s College of Engineering & Technology, Navi Mumbai, India Ghanshyam Jindal MGM's College of Engineering and Technology Uttam Bagal MGM's College of Engineering and Technology, Navi Mumbai, India Gajanan Nagare Department of Biomedical Engineering, Vidyalankar Institute of Technology, Mumbai, India 2022 11 04 2023 02 07 Purpose: Medical professionals throughout the world prefer to use conventional stethoscopes to listen to respiratory sounds. Listening to respiratory sounds through stethoscopes is a subjective matter, and proper diagnosis of the disease depends on the skills and ability of the doctor. Computerized analysis of respiratory sounds can help doctors and researchers to characterize different abnormal respiratory patterns and make informed decisions. Materials and Methods: This study includes previously reported work in different normal and abnormal respiratory sounds. The IEEE, PubMed, Google Scholar and Elsevier databases were searched and studies with the keywords of lung sound analysis, respiratory sound analysis, and respiratory sound classification were included. Detailed characteristics of normal and abnormal respiratory sounds are mentioned. In addition, Time-amplitude characteristics of different respiratory sound plots are obtained using MATLAB and ICBHI database. This study systematically discusses different approaches for respiratory sound analysis like visual analysis of the time-amplitude signals, frequency analysis, and spectral analysis using fast Fourier transform, statistical analysis, and machine learning approach. A list of relevant datasets is mentioned that can help researchers to do further analysis in this domain. Results: The careful observations and analysis show the possibility of predicting respiratory diseases by extracting suitable parameters such as the frequency response and spectral characteristics of the signal. Power spectral density can help us to calculate the maximum, median frequency over an extended period. Using machine learning we can estimate the energy, entropy, spectral features, and wavelets of the signals. Conclusion: Computer-based respiratory sound analysis can help medical professionals in making informed decisions. This will help in early diagnosis and devise effective treatment plans for the patients. https://fbt.tums.ac.ir/index.php/fbt/article/view/586 https://fbt.tums.ac.ir/index.php/fbt/article/download/586/402

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 11 2 2023 09 21 199 206 Maliha Rashida Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong-4349, Bangladesh Mohammad Ashfak Habib Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong-4349 2022 10 15 2023 02 04 Purpose: Independent Component Analysis (ICA) decomposition is a commonly used technique for eye blink artifact detection from Electroencephalogram (EEG) signals. Feature extraction from the decomposed ICs is a prime step for blink detection. This paper presents a new model of eye blink detection for ICA based approach, where the decomposed ICs are projected to their corresponding EEG segments (ReEEG), and feature extraction is performed on the ReEEG instead of the IC. ReEEG represents the eye blink activity more distinctly. Hence, ReEEG-based feature extraction is more potential in detecting eye blink artifacts than the traditional IC-based feature extraction. Materials and Methods: This paper employs twelve EEG features to substantiate the superiority of ReEEG over IC. Support Vector Machine (SVM) is used as a classifier. A dataset, having 2638 clinical EEG epochs, is employed. All the considered twelve features are extracted from ReEEG and fed to SVM one at a time for blink detection. Then the obtained results are compared with an IC-based model with the same features. Results: The comparison reveals the success of the proposed ReEEG-based blink detection approach over the traditional IC-based approach. Accuracy, precision, recall, and f1 scores are calculated as performance measuring metrics. For almost all features, ReEEG-based approach achieved up to 12.25% higher accuracy, 24.95% higher precision, 13.49% higher recall, and 12.89% higher f1 score than the IC-based traditional method. Conclusion: The proposed model will be useful for researchers in dealing with the eye blink artifacts of EEG signals with more efficacy. https://fbt.tums.ac.ir/index.php/fbt/article/view/569 https://fbt.tums.ac.ir/index.php/fbt/article/download/569/354

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 11 2 2024 03 31 207 214 Arman Saeedivahdat Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Tabriz University of Medical Sciences, Tabriz, Iran Tohid Babaei Postgraduate Student,Department of Periodontology,Faculty of Dentistry,Qazvin University of Medical Sciences,Qazvin,Iran Nadereh Ariamanesh Department of Restorative Dentistry, Faculty of Dentistry, Qazvin University of Medical Sciences, Qazvin, Iran Hadi Monzavi Department of Prosthodontics, Faculty of Dentistry, Qazvin University of Medical Sciences, Qazvin, Iran Pouya Badri Department of Orthodontics, Faculty of Dentistry, Qazvin University of Medical Sciences, Qazvin, Iran Ali Nokhbeh Department of Periodontology, Faculty of Dentistry, Qazvin University of Medical Sciences, Qazvin, Iran 2023 01 20 2023 02 05 Purpose: The present study aims to assess the differences in the condyle position for two skeletal classes using Cone-Beam Computed Tomography (CBCT) reconstructions for both sides and genders. Materials and Methods: In this cross-sectional descriptive study, the CBCT images of 96 patients (20-60 years) were assessed. The participants were divided according to their Angle malocclusion classifications (Angle Classes I and III). The variables of the Anterior-Posterior position of the Condyle (APC), condylar angle in the axial plane (ACA), the Lateral Position of the Condyle in the axial plane (LPC), the Vertical Position of the Condyle (VPC), condylar angle in coronal dimension (CCA), and the difference of APC and VPC on both sides were measured. The measurements were analyzed using a one‑way ANOVA and Tukey’s post hoc test. Results: The variables of APC, LPC, ACA, VDC, and the difference of the APC on both sides in the two skeletal classes were similar. The VPC and CCA were greater in Class III than in Class I. All variables representing the 3D position of the condyle were similar in men and women, as well as on the right and left in both skeletal classes, I and III. Conclusion: Based on the 3D evaluation results of the condylar position, the skeletal classes III and I differed in the VPC and CCA; however, for the rest variables, there were no statistical differences. https://fbt.tums.ac.ir/index.php/fbt/article/view/634 https://fbt.tums.ac.ir/index.php/fbt/article/download/634/304 https://fbt.tums.ac.ir/index.php/fbt/article/download/634/307

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 11 2 2024 03 31 265 277 Ali Ekhlasi Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Ali Motie Nasrabadi Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran Mohammadreza Mohammadi Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran 2024 04 22 2024 08 25 Purpose: Investigating brain connectivity using Electroencephalogram (EEG) is a valuable method for studying mental disorders, such as Attention-Deficit/Hyperactivity Disorder (ADHD), and optimizing and developing measures of effective connectivity can provide new insights into differences in brain communication in such disorders. Multivariate Transfer Entropy (MuTE) is a measure of causal connectivity that quantifies the influence of multiple variables on each other in a system. In this study, the MuTE measure was modified by incorporating an interaction delay parameter in connectivity calculations to create a measure with self-prediction optimality, which we named . Materials and Methods: We applied  to investigate EEG effective connectivity in healthy and ADHD children performing an attention task across five frequency bands and to compare brain connectivity differences between the two groups using statistical analysis. Results: Our analysis revealed that children with ADHD exhibited excessive short-distance connections in all frequency bands while healthy children demonstrated stronger long-range connections in the alpha and gamma frequency bands. Moreover, excessive short-distance connectivity was observed in the delta and theta frequency bands in all brain regions, as well as in the alpha, beta, and gamma frequency bands between the central and parietal regions in children with ADHD. These connectivity patterns may contribute to impaired attention functions by impeding effective information transmission and reducing information processing speed in the brains of children with ADHD. Conclusion: Our analysis presents a novel methodology for measuring effective connectivity and elucidates the differences in EEG brain connectivity between children with ADHD and healthy children. https://fbt.tums.ac.ir/index.php/fbt/article/view/998 https://fbt.tums.ac.ir/index.php/fbt/article/download/998/468

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 278 291 EN Ali Ekhlasi Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Hessam Ahmadi Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Mohammad Saleh Hoseinzadeh Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran 2024 06 27 2024 12 25 Purpose: Brain-Computer Interfaces (BCI) are advanced systems that enable a direct neural pathway between the human brain and external devices. The importance of BCI is underscored by its profound implications for medical therapeutics, particularly in neurorehabilitation. Materials and Methods: This study developed an algorithm to detect 8 motion commands for a robot using individuals' EEG signals (Electroencephalogram). These signals were recorded during imagined and expressed commands. The research aimed to identify optimal features for extracting and classifying EEG signals for robot commands and to pinpoint the best EEG channels for a cost-effective, efficient signal acquisition system. Four categories of features, including temporal, frequency, wavelet, and combined features were extracted from the EEG signals. The Imperialist Competitive Algorithm (ICA) and Cuckoo Optimization Algorithm (COA) were utilized for feature selection. Results: Findings revealed that wavelet features are most effective for analyzing and classifying EEGs. For imagined commands, optimal features from all channels achieved a 96.3% classification accuracy, while expressed commands reached 96.5%. The frontal and parietal lobes were identified as the prime EEG channels for command detection, achieving accuracies of 91.5% and 86.9% for imagined commands, and 92.7% and 86.1% for expressed commands, respectively. The result also indicated that the brain's midline and left hemisphere (containing the Broca area) outperformed the right hemisphere in classification. Conclusion: By focusing on the optimal EEG channels, a more cost-effective hardware system can be designed, surpassing the traditional 21-channel system and requiring only 14 electrodes in the frontal and parietal regions. https://fbt.tums.ac.ir/index.php/fbt/article/view/1050 https://fbt.tums.ac.ir/index.php/fbt/article/download/1050/477

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 292 308 Ritika Upadhyay School of Engineering, Ajeenkya DY Patil University, Pune, Maharashtra 412105, India Biswajeet Champaty School of Engineering, Ajeenkya DY Patil University, Pune, Maharashtra 412105, India Suraj Nayak Department of Electrical and Electronics Engineering, School of Engineering and Technology, ADAMAS University, Kolkata, West Bengal, 700126, India 2023 02 06 2023 10 12 Purpose: This study aims to determine whether Himalayan singing bowl vibrations could lead to deeper and faster relaxation than supine silence. Numerous civilizations have used singing bowls, gongs, bells, didgeridoos, and voice sounds and chants as instruments for sound healing for ages in religious rites, festivals, social celebrations, and meditation activities. Materials and Methods: The effect of sound vibrations on physical and mental wellness is supported by scientific research. Although various pieces of research have demonstrated the effect of meditation on humans, very few studies have been done on the beneficial effects of singing bowls on the body and the mind (decrease in unease and temperament, Electroencephalogram, etc.). This study suggests two Machine Learning (ML) models for the automatic classification of the meditative state from the normal state using the Heart Rate Variability (HRV) data. Results: To pick suitable inputs for the ML models a statistics-based t-test and Principal Component Analysis (PCA) was applied. In the statistics-based t-test method, the HRV parameters were subjected to choose appropriate input for the ML model. Conclusion: In this case study there are two models that were considered the most effective models based on their accuracy, that are MLP 31-13-2 and RBF 31-17-2 model having a training accuracy of 83.75% and 68.75% respectively. In the second case study, the PCA approach was applied to the HRV parameters, and as a result MLP 4-6-2 and MLP 4-10-2 were the most effective models, with an accuracy of 69.6% and 71.4% respectively. https://fbt.tums.ac.ir/index.php/fbt/article/view/644 https://fbt.tums.ac.ir/index.php/fbt/article/download/644/489

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 309 319 EN Anahita Jafari Medical Imaging Research Centre, Shiraz University of Medical Sciences, Shiraz , Iran Fariba Zarei Medical Imaging Research Centre, Shiraz University of Medical Sciences, Shiraz 7193635899, Iran Hamidreza Masjedi Medical Imaging Research Centre, Shiraz University of Medical Sciences, Shiraz 7193635899, Iran Samira Moshiri Medical Imaging Research Centre, Shiraz University of Medical Sciences, Shiraz 7193635899, Iran Vyas Akondi Department of Physical Sciences, Indian Institute of Science Education and Research (IISER) Berhampur, Berhampur, Odisha 760010, India Sabyasachi Chatterjee Retired Scientist from Indian Institute of Astrophysics, Present Affiliation: Ongile, 79 D3, Sivaya Nagar, Reddiyur Alagapuram, Salem 636004. India Rezvan Ravanfar Haghighi Medical Imaging Research Centre, Shiraz University of Medical Sciences, Shiraz , Iran 2024 02 18 2024 05 30 Purpose: The purpose of this study is to test the linearity of the CT system and ascertain the relationship between Hounsfield Unit ( ) values and weight/weight concentrations of iodine ( ) in mixtures. This aims to determine the iodine concentration thresholds for achieving effective contrasts with minimal iodine usage. Materials and Methods: Aqueous solutions of Iopaque, with 300mgI/mL of iodine, were prepared for different weight/weight ( ) iodine concentrations and filled in a water-pool phantom, and the  observations were taken at different kVps for 10 different CT machines. The variation of  with  was analyzed as, . From this, the  necessary for getting a required  value is estimated. Results: It is found that HU(V) varies linearly with  for low wi values, although the coefficients  and  vary widely between machines. For optimal HU enhancement, it was found that a 0.01% weight/weight concentration of iodine is adequate to produce an  value of 450 at 80 kVp, while the corresponding concentration should be 0.025% weight/weight at 120 kVp. Conclusion: Linear dependence of HU on wi helps to reduce the contrast media volume by estimating the iodine concentration, necessary for obtaining a required HU. It also revealed that lower kVps could yield adequate HU enhancement with a reduced contrast agent, thus potentially minimizing patient exposure to radiation and contrast media. https://fbt.tums.ac.ir/index.php/fbt/article/view/940 https://fbt.tums.ac.ir/index.php/fbt/article/download/940/496

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 320 340 EN Shabnam Andalibi Miandoab Department of Electrical Engineering, Tabriz Branch, Islamic Azad university, Tabriz, Iran Nazlar Ghasemzadeh Department of Biomedical Engineering, Tabriz Branch, Islamic Azad university, Tabriz, Iran 2024 10 08 2024 11 26 Purpose: In optogenetics, visible light is usually used, which limits the penetration depth into the tissue, and placing optical fibers to deliver light to deep areas of the brain is necessary. In this paper, to overcome limitations, the use of Near-Infrared light (NRI) and temperature-sensitive opsins has been proposed as a powerful, non-invasive, or minimally invasive tool due to greater penetration depth, with the least damage and most effectiveness in brain tissue. Materials and Methods: Effects of optogenetic stimulation with visible light and NIR on the model of Parkinson's Disease (PD) Basal Ganglia-Thalamic (BG-Th) network to reduce or eliminate pathological effects of Parkinson's disease has been studied. Three and four-state optogenetic Halordopsin (NpHR) and Channelrhodopsin-2 (ChR2) opsins at visible wavelengths and four-state optogenetic with Transient Receptor Potential Vanilloid 1 (TRPV1)  and Transient Receptor Potential Ankyrin 1 (TRPA1) opsins at NIR wavelengths for different frequencies and number of stimulation pulses and light intensity on Error Index (EI) and beta band activity in the BG-TH to introduce optimal values for basic parameters of f, ns, and Alight have been considered. Finally, we obtained Alight effects on the beta band activity for different optogenetic stimulations and opsins (NpHR, ChR2, TRPV1, and TRPA1). Results: Four-state optogenetic stimulation TRPA1 at 808 nm is optimal with the best results, lowest EI, and beta band activity. By increasing Alight, beta band activity for all used opsins has decreased, which is sharp for NpHR, and TRPA1 with 808 nm, with low intensity, has caused less beta band activity. Conclusion: The Near-Infrared light with the best results and the lowest beta band activity (Beta activity=0.2) is more effective. https://fbt.tums.ac.ir/index.php/fbt/article/view/1125 https://fbt.tums.ac.ir/index.php/fbt/article/download/1125/467

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 341 354 Mohamed Belmokeddem Biomedical Engineering Department, Faculty of Technology, University Abou-Bekr Belkaid of Tlemcen, Algeria Kamila Khemis Biomedical Engineering Department, Faculty of Technology, University Abou-Bekr Belkaid of Tlemcen, Algeria Salim Loudjedi Surgery B Tlemcen Hospital, Department of Medicine, University Abou-Bekr Belkaid of Tlemcen, Algeria 2023 11 07 2024 03 04 Purpose: Ensuring excellent video quality is crucial for the success of minimally invasive surgical procedures without disrupting the surgical procedure flow. Real-time laparoscopic video frequently encounters issues such as blur and smoke, often stemming from lens contamination. The automatic detection of these distortions is imperative to assist surgeons, ultimately reducing operative time and mitigating risks for the patient. Materials and Methods: In this paper, we leverage the Laparoscopic Video Quality (LVQ) database developed by Khan et al. to train and validate our model. To classify defocus blur, motion blur, and smoke in the laparoscopic video, we adopt a novel approach utilizing a cascade support vector machine (SVM) classifier, which combines decisions from three binary classifiers. The first classifier categorizes videos into two classes: good and distorted. The second classifier focuses on detecting smoke and blur, while the third is dedicated to distinguishing between defocus blur and motion blur. Results: In this study, we calculate performance metrics, including accuracy rate, precision, recall, F1 score, and execution time, which are crucial indicators for evaluating quality detection results. The machine-learning classification demonstrates notable performance, with an accuracy rate of 96.55% for the first classifier, 100% for the second, and 99.67% for the third classifier. Additionally, the classification achieves a high inference speed of 37 frames per second (fps). Conclusion: The experimental results showcased in this paper underscore the efficacy of the proposed approach in automatically detecting distortions in a laparoscopic video. The method exhibits high performance, excelling in both accuracy and processing speed. Notably, the method's advantage lies in its simplicity and the fact that it does not necessitate high-performance computer hardware. https://fbt.tums.ac.ir/index.php/fbt/article/view/873 https://fbt.tums.ac.ir/index.php/fbt/article/download/873/464

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 355 364 EN Mahmoud Bagheri Department of Medical Physics, School of Paramedicine, Arak University of Medical Sciences, Arak, Iran Javad Hosseini Nejad Neuroscience research center, Baqiyatallah University of Medical Sciences, Tehran, Iran Hassan Tavakoli Department of Physiology and Biophysics, Baqiyatallah University of Medical Sciences, Tehran, Iran Seyed Abbas Tavallaie Behavioral Sciences Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran Aliakbar Karimi Zarchi Department of Epidemiology and Biostatistics, Baqiyatallah University of Medical Sciences, Tehran, Iran 2023 11 25 2023 12 26 Purpose: This study was conducted to evaluate the comparative effectiveness of repetitive Transcranial Magnetic Stimulation (rTMS) and intermittent Theta Burst Stimulation (iTBS), in Treatment-Resistant Depression (TRD) patients using resting-state Electroencephalography (EEG). iTBS is a novel form of magnetic stimulation with the potential to produce similar anti-depressant effects but in a much shorter time. Materials and Methods: In two stimulation protocols, 78 patients with TRD received 20 sessions. Depression symptoms were assessed based on the changes in the Hamilton Depression Rating Scale (HAM-D) and Beck Depression Inventory (BDI-II) scores at baseline, after the last session, and at 4 weeks after treatment. Resting-state EEG was measured at baseline and after the last session. EEG power spectrum was extracted and power changes were evaluated statistically. Results: There was no significant difference in response and remission rates between the two groups. Following 10 Hz rTMS and iTBS, the clinical indexes improved by 48.5 ± 19.8 % (p-value < 0.05) and 50.4 ± 21.7 % (p-value < 0.05), respectively. There was a significant reduction in the mean depression scores for both treatment groups (p < 0.05). Following treatment, TRD patients showed considerable enhancement in gamma power at the left DLPFC site (F3, F5, and F7 electrode) in the iTBS group and significant increases in delta power at the F3 and F7 electrode sites in the 10 Hz rTMS group. Conclusion: iTBS provides clinical advantages, which showed that the results did not contrast altogether with results from a standard course of rTMS treatment. It might be invaluable from a clinical, benefit, and understanding perspective. Biomarkers of clinical outcomes such as resting-state brain activity measured with EEG may save individuals worthless treatment and moderately limited clinical assets. https://fbt.tums.ac.ir/index.php/fbt/article/view/888 https://fbt.tums.ac.ir/index.php/fbt/article/download/888/390

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 365 375 Fatemeh Ziyaei Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Somaye Malmir Department of Physics, Payame Noor University, P. O. Box 19395-4697, Tehran, Iran Raheleh Tabari Juybari Department of Radiology Technology, Behbahan Faculty of Medical Sciences, Behbahan, Iran Masoumeh Dorri Giv Nuclear Medicine Research Center, Department of Nuclear Medicine, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran Maryam Yaftian Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran 2023 10 29 2023 12 02 Purpose: The breast is a radiosensitive organ and it is important to prevent the Contralateral Breast (CLB) from irradiation in radiotherapy. In this study, the received dose of CLB was calculated and compared between two breast radiotherapy techniques, including physical stationary and motorized wedged fields. Materials and Methods: Forty female patients undergoing breast radiotherapy with supraclavicular involvement were randomly selected. Twenty were treated with the tangential fields using physical wedges and twenty patients were treated with the tangential fields using motorized wedges. Three thermo-luminescent dosimeters (TLD GR-200) were placed on the CLB skin to estimate the breast dose. Dosimetric parameters for target tissue and organs at risk (OARs) were obtained from the plans of the evaluated techniques and compared to find the differences. CLB doses were compared between the radiotherapy techniques using an independent T-test. Results: There were no significant differences in the target tissue and OARs dosimetric parameters between the evaluated radiotherapy techniques. The results showed that the measured CLB skin doses in patients treated with the motorized wedges were significantly higher than the physical wedge radiotherapy technique, 201.5±20.4 mGy vs. 159.8 ±14.2 mGy (P<0.05).Conclusion: The physical wedged fields technique had lower doses for CLB compared to the fields using motorized wedges. Therefore, it can be proposed to use tangential physical wedged fields for patients with high concern about the CLB. Furthermore, more research considering radiotherapy techniques without using wedges in medial tangent fields and other relevant parameters can be performed to obtain a better evaluation of the CLB dose. https://fbt.tums.ac.ir/index.php/fbt/article/view/858 https://fbt.tums.ac.ir/index.php/fbt/article/download/858/415

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 376 386 Mahya Naghipoor-Alamdari Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran Jafar Zamani School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran Farzaneh Keyvanfard School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran Abbas Nasiraei-Moghadam Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran 2023 11 02 2023 12 09 Purpose: Schizophrenia (SZ), which affects 0.45% of adults worldwide, is a complex mental illness with unknown causes and mechanisms. Neuroimaging techniques have been used to study changes in the brain of patients with SZ. In this study, we aim to construct brain subnetworks, analyze the association of structure with function, and investigate them with graph measures. We hope to identify important subnetworks and graph measures for SZ diagnosis. Materials and Methods: This study investigates the structural and functional brain connectivity of 27 healthy controls (HC) and 27 patients with SZ. Independent component analysis (ICA) and joint ICA (jICA) are used to construct subnetworks based on functional and structural connectivity. An association between structural and functional connectivity is examined. Joint functional and structural subnetworks are also examined and compared with independent analysis of functional and structural subnetworks. Several graph measures are used in the whole brain and its subnetworks. Results: In this study, we investigated brain connectivity in HC and SZ patients using graph measures. The study analyzed both the whole brain and brain subnetworks to better understand the importance of partitioning the brain into subregions. Our results suggest that analyzing whole brain may not be the most effective method for studying brain peculiarities of SZ patients. In addition, multimodal brain analysis has proven to be effective in understanding SZ. There is no one-to-one relationship between structural and functional connectivity in the brain. Certain measures such as maximum modularity, clustering coefficient, network strength, global efficiency and path length were important in distinguishing patients with SZ from HCs in specific subnetworks. This study recommends further investigation of specific subnetworks that overlap with default mode, visual, and somatomotor resting state networks. Conclusion: This study emphasizes importance of subnetwork and multimodal analysis for understanding SZ disease. https://fbt.tums.ac.ir/index.php/fbt/article/view/870 https://fbt.tums.ac.ir/index.php/fbt/article/download/870/386

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 387 398 Bahador Bahadorzadeh Nuclear Engineering Department, School of Mechanical Engineering, Faculty of Engineering, Shiraz University, Shiraz, Iran Reza Faghihi Nuclear Engineering Department, School of Mechanical Engineering, Faculty of Engineering, Shiraz University, Shiraz, Iran Sedigheh Sina Nuclear Engineering Department, School of Mechanical Engineering, Faculty of Engineering, Shiraz University, Shiraz, Iran Ahdiyeh Aghaz Radiation Application Research School, Nuclear Science and Technology Research Institute, Tehran, Iran Arman Rahmim Nuclear Engineering Department, School of Mechanical Engineering, Faculty of Engineering, Shiraz University, Shiraz, Iran Mohammad Reza Ay Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran 2023 09 24 2023 12 02 Purpose: The Xtrim-PET preclinical scanner is specifically designed for positron emission tomography (PET) imaging of small laboratory animals. This study aims to increase the spatial resolution of the scanner by implementing gantry wobbling. Materials and Methods: The gantry wobbling was evaluated using the Gate Monte Carlo code. To prevent image blurring during gantry wobbling, all locations detected in the 3D output were corrected in the sinogram matrix according to the coincidence time of annihilation photons and the gantry motion. In order to evaluate the performance of the scanner using the wob