Background:Alzheimer's disease (AD) is characterized by progressive neurodegeneration in regionally vulnerable brain areas, yet molecular insights into early pathogenic mechanisms remain limited. Methods:We conducted a meta-analysis of transcriptomic datasets from brain regions affected in early-to-moderate AD - including entorhinal cortex, CA1 hippocampus, angular gyrus, and frontal cortex synaptoneurosomes - using data from seven mRNA and one microRNA (miRNA) microarray studies (GSE16759, GSE110226, GSE37264, GSE26972, GSE36980, GSE37263, GSE39420, and GSE157239). Preprocessing included background correction, log2 transformation, quantile normalization, and batch correction via ComBat. Differentially expressed features were defined as false discovery rate <0.05 and | logFC| ≥ 1.23 (genes) or ≥ 2 (miRNAs). Results:We identified 172 differentially expressed genes (122 upregulated and 50 downregulated) and 82 significant miRNAs. Hub genes included Inositol-trisphosphate 3-kinase B (ITPKB), Synaptotagmin 1, Dystrobrevin alpha (DTNA), X Inactive Specific Transcript, and Regulator of G protein signaling 4 (RGS4). Functional enrichment highlighted calcium signaling, synaptic failure, and neuroinflammation. Notably, hsa-miR-30d-5p was predicted to target both ITPKB and DTNA, suggesting a regulatory axis linking miRNA dysregulation to calcium dyshomeostasis. Receiver operating characteristic analysis revealed that only RGS4 showed moderate discriminative capacity (area under the curve [AUC] =0.70), while other hub genes (e.g., ITPKB, AUC = 0.40) exhibited below-chance performance, underscoring the limitations of single-gene classifiers in postmortem tissue. Conclusion:This study provides mechanistic hypotheses - rather than diagnostic biomarkers - by uncovering region-specific, miRNA-mediated regulatory networks in AD-affected brain tissues. Future validation in accessible biofluids is essential before clinical translation.
Background:The neuromuscular training is a subgroup of functional exercises. The effectiveness of the neuromuscular training substantially improves in combination with electromyography (EMG) biofeedback. The present study aimed to investigate the effect of a new model of neuromuscular training in knee osteoarthritis (KOA).Methods:This pilot, parallel randomized-clinical-trial involved 10 participants with moderate KOA, who were randomly assigned into either neuromuscular training for the gluteus maximus and gastrocnemius (pro-group = 5) or the quadriceps (against-group = 5). Muscle activity in the feedback phase of gait (EMG), pain (Visual Analog Scale [VAS] and knee injury and osteoarthritis outcome score [KOOS]-pain score), and function (average walking speed and KOOS score) were assessed at baseline, immediately after the first treatment session (except KOOS), after 10 sessions of intervention, and after a 3-month of follow-up.Results:After the first treatment session, pain slightly (5.71%) increased in the against-group, whereas decreased by 6.45% in the Progroup; after the 10th session and 3 months, all variables in both groups improved, with a slight extra positive difference in the Pro-group. However, after 3 months, the percentage of changes in the Progroup was greater than that of the against-group. The pain intensity based on VAS (-96.8%), pain (+101.30%), and quality of life (+145.41%) scores of the KOOS questionnaire showed nearly 100% or even greater improvement in the pro-group.Conclusion:Retraining quadriceps and gastrocnemius and gluteus maximus using biofeedback during gait seems promising in KOA, although the Progroup apparently experienced considerably greater clinical improvement. The study provides preliminary evidence of the clinical feasibility of a novel neuromuscular training paradigm for KOA based on biofeedback during gait.Trial Registration:This study was registered under the International Randomized Controlled Trial Number registry on November 20, 2023.
Background:Coronary artery disease (CAD) remains the leading global cause of cardiovascular mortality. Although single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is widely used, progress in artificial intelligence (AI)-based diagnostic tools is constrained by the limited availability of modern, high-quality, and consistently labeled imaging datasets. Methods:We retrospectively analyzed 144 rest-stress MIBI (99m Tc-methoxy isobutyl isonitrile) SPECT MPI studies acquired using ASNC/EANM-compliant protocols. Images were reconstructed using filtered back projection and independently interpreted by three nuclear cardiology specialists. Clinical, demographic, and imaging variables were analyzed using SPSS v26, with P < 0.05 defining statistical significance. Results:Significant sex-specific differences in CAD presentation were observed. Women with CAD were older and more frequently demonstrated perfusion patterns compatible with microvascular dysfunction, whereas men exhibited larger territorial defects. Diabetes prevalence was significantly higher among CAD-positive patients, whereas smoking patterns differed markedly by sex. Family history of CAD was significantly more common among CAD-positive subjects. Perfusion abnormalities correlated strongly with cumulative cardiovascular risk burden. Interobserver agreement was excellent (Intraclass correlation coefficient (ICC) = 0.87). Conclusions:We introduce a rigorously standardized, clinically annotated SPECT MPI dataset tailored for developing and validating explainable AI models in nuclear cardiology. Its sex-stratified structure supports research into CAD phenotypes and enhances reproducibility.Public Access:https://misp.mui.ac.ir/fa/t2-dual-head-spectct-coronary-artery-disease-cad.
Background:The present study aimed to identify and analyze the psychological, cognitive, and neurophysiological factors influencing success in theta/alpha neurofeedback training. The research focused on how personality dimensions (Myers-Briggs Type Indicator), impulsivity (UPPS), intelligence quotient (Raven's Progressive Matrices), and baseline EEG frequency bands relate to neural self-regulation performance.Methods:A quantitative descriptive-analytical design was employed. Data from six healthy participants who completed eight neurofeedback sessions were collected and analyzed using a multilayer perceptron (MLP) neural network and Elastic Net regression implemented in Python.Results:Findings revealed consistent increases across EEG frequency bands, with baseline neurophysiological measures sufficient for predicting training outcomes. The Elastic Net analysis identified the Judging personality trait, impulsivity, and baseline delta power as the most influential predictors of responsiveness. Furthermore, enhanced negative correlations between theta and alpha bands suggested improved cognitive differentiation during training.Conclusion:Neurofeedback responsiveness is a multifaceted phenomenon influenced by both neurophysiological indices and psychological-cognitive factors. These results underscore the importance of integrating psychological profiling with neural data to optimize individualized neurofeedback interventions.
Background:This study evaluates the performance of five deep convolutional neural networks (DCNNs) for supervised segmentation of white matter (WM) and gray matter (GM) in brain positron emission tomography (PET) images using label maps derived from corresponding magnetic resonance (MR) images. The goal is to reduce dependency on hybrid PET/Magnetic resonance imaging (MRI) by extracting anatomical information solely from PET images, thereby providing a potential pathway for MRI-free partial volume correction (PVC).Methods:A total of 300 PET images and their corresponding MR-derived labels from the OASIS-3 dataset were used. The five DCNNs, including the UNet, RegUNet, VNet, SegResNet, and HighResNet, were implemented within the Medical Open Network for Artificial Intelligence (MONAI) framework. The networks were evaluated using Dice score and Intersection over Union (IoU) metrics.Results:Among the networks, VNet demonstrated superior performance for GM and WM segmentation, achieving Dice scores of 61.18% and 76.23%, and IoU scores of 44.15% and 61.62%, respectively. UNet and VNet showed competitive performance for WM segmentation, with no statistically significant differences between them.Conclusions:These findings provide insights into the performance of DCNNs for PET image segmentation, highlighting VNet's capability and emphasizing the potential for optimizing segmentation techniques for PVC applications.
Background:Sparse representation (SR) has shown strong performance in classification tasks, particularly for high-dimensional data such as microarray gene expression profiles. These datasets present significant challenges due to their high dimensionality and limited sample size, which often hinder the performance of conventional classifiers.Methods:SR addresses this by expressing each signal as a linear combination of a small subset of training samples, reducing computational complexity and improving accuracy. However, using all training samples in the dictionary increases computational cost. This study explores several SR-based classifiers to address microarray data classification, focusing on dictionary construction strategies and sparse coding algorithms.Results:Experimental results on the 14-Tumors dataset show that selecting a subset of representative atoms and applying the SL0 algorithm significantly improves both speed and classification accuracy.Conclusions:These findings highlight the potential of SR approaches for effective and efficient classification of high-dimensional biological data.
Background:Digitizing electrocardiogram (ECG) images into structured time-series data is critical for clinical analysis, but it remains challenging due to the lack of standardized datasets, especially under realistic scenarios like overlapping waveforms. Methods:We introduce SynthECG, an open-source Python framework to generate four synthetic ECG datasets tailored for deep learning tasks, including ECG digitization, YOLO-based lead and lead name detection, and U-Net-based waveform segmentation. The framework supports customizable parameters (e.g., dataset size, lead layout, and visual style) and allows generating up to 21,799 images for multi-lead datasets and 261,588 for single-lead segmentation. Notably, it introduces a novel mechanism to simulate overlapping waveforms from adjacent leads while preserving clean segmentation masks. Results:Using our framework, we generated four open-access datasets: (1) 2000 ECG images in various lead configurations paired with time-series signals for ECG digitization, (2) 2000 ECG images in various lead configurations with YOLO-format annotations for detecting lead regions and lead names, (3) 20,000 cropped single-lead images with pixel-level segmentation masks (normal variant), and (4) 102 cropped single-lead images with overlapping waveforms from adjacent leads (overlapping variant). We validated these datasets through two case studies: digitization using a non-ML algorithm (mean squared error: 0.002, ρ: 0.93, signal-to-noise ratio [SNR]: 7.36 dB, SNRmed: 37.86 dB) and lead/name detection using YOLOv8. Conclusions:Our open-source framework enables the generation of large-scale, customizable ECG image datasets to support key deep learning-based tasks, including digitization under normal and overlapping conditions, as well as lead region and lead name detection. The full datasets and code are publicly available at: https://doi.org/10.5281/zenodo.15484519 and https://github.com/rezakarbasi/ecg-image-and-signal-dataset.
Background: The respiratory system of individuals with coronavirus 2019 (COVID-19) experiences significant strain due to the body’s immunological response and inflammation, leading to organ failure. Over time, patients have improvement in symptoms such as radiological abnormalities, pulmonary function impairment, and decline in respiratory and physical abilities. This research aimed to assess the impact of COVID-19 on the pulmonary function and physiological performance capacity of patients with moderate and severe problems who were hospitalized and subsequently released. These parameters were evaluated during 3, 6, and 12-month follow-up periods. Methods: The participants in this research were individuals hospitalized with COVID-19 at Hajar Shahrekord Hospital. They evaluated using spirometry tests, a 6-min walk test (6MWT), and spiral lung high-resolution computed tomography (HRCT) scans. The assessments were conducted from their first diagnosis until 12 months after release. The sampling was performed employing the head-counting approach, and an expert examined the data collected from spirometry and 6MWT in statistics and epidemiology. Expert radiologists and pulmonologists examined spiral lung HRCT data. Results: The combined data from spirometry (revealing improved lung function), the 6MWT (showing increased endurance), and HRCT (indicating reduced lung damage) demonstrated marked progress in both groups throughout the study period. While each group demonstrated statistically significant improvements at various follow-up points, no significant difference emerged between the moderate and severe patient groups in the study. Significant improvements in lung function, physical capacity, and radiological outcomes were observed in both moderate and severe COVID-19 patients at the 12-month follow-up. Notably, there were no statistically significant differences in improvement between the groups. Conclusion: This study emphasizes the importance of personalized extended care and rehabilitation for patients severely affected by COVID-19, aiming to tackle ongoing deficits and prevent long-term complications.
Background: Uncertainty in medical images—especially mammograms—caused by low contrast and insufficient brightness creates difficulties in detecting masses and microcalcifications. These limitations often lead to diagnostic uncertainty for radiologists, making effective image enhancement essential for accurate clinical assessment. Aims and Objectives: This study aims to develop an improved method for digital mammography image enhancement that reduces uncertainty, improves contrast, and preserves fine details to support more accurate diagnosis. Materials and Methods: The proposed method integrates intuitionistic fuzzy entropy and neutrosophic sets (NSs) in a five-stage framework: (1) Transforming the input image into an intuitionistic fuzzy set; (2) Applying intuitionistic fuzzy entropy to reduce ambiguity; (3) Converting the image to an NS representation; (4) Enhancing image details using the neutrosophic divergence score (NDS); (5) Improving contrast through fuzzy histogram hyperbolization. Performance was evaluated on two benchmark mammography datasets using quantitative metrics, including the contrast improvement index, discrete entropy, absolute mean brightness coefficient, absolute mean brightness error, and the naturalness image quality evaluator, as well as a qualitative visual assessment. Results: Experimental results show that the proposed method surpasses existing approaches, including intuitionistic fuzzy sets, type-2 fuzzy sets, and neutrosophic-based enhancement methods. It achieves superior contrast enhancement, preserves naturalness, and effectively highlights fine mammographic details. Conclusion: The method substantially reduces uncertainty in mammography images, enhancing diagnostic visibility and supporting improved accuracy for radiologists. Its strong performance across fatty, fatty-glandular, and dense-glandular breast tissues makes it a promising component for future computer-aided diagnosis systems.
Background: Addiction is one of the critical problems in public health. The lateral habenula (LHb) is a brain structure that plays an important role in sleep, reward-based decision-making, punishment avoidance, and stress. Based on the function of the LHb in addiction, this study examined the impacts of electrical stimulation (ES) and temporary inactivation of the LHb by lidocaine on the process of morphine addiction. Methods: The anesthetized animals were placed in the stereotaxic device. A cannula and electrode were inserted into the LHb for stimulation at both low and high intensities (LI: 25 and HI: 150 μA), and injecting lidocaine, respectively. Then, jugular vein was catheterized. After recovery, an 11-day self-administration protocol was performed. Animals received morphine or saline during each session. Finally, the counts of both active and passive lever presses, along with the instances of self-infusions, were documented and assessed. Results: Morphine led to an increase in the number of active lever presses in the morphine group compared to the saline group (P < 0.001). HI-ES and lidocaine injection decreased the changes compared to the morphine group (P < 0.001). In addition, the number of infusions in the morphine group was higher than the saline group after the 6th day (P < 0.001); HI-ES and lidocaine injections reduced the alterations relative to the morphine group ([P < 0.006], [P < 0.001], respectively). Conclusion: The results indicate that HI-ES of the LHb and lidocaine injection into this region reduce morphine self-administration and active lever pressing. These findings underscore the prominent role of the LHb in regulating reward-related behaviors and drug consumption.
Background:This paper introduces an approach for dimensionality reduction and classification of electroencephalogram signals in motor imagery brain-computer interface (MI-BCI) systems. Materials and Methods:The proposed Kron-reduced generic learning regularization with differential evolution (K-GLR-DE) framework leverages graph signal processing (GSP) with a meta-heuristic optimizer, integrating functional clustering, Kron reduction, regularized common spatial patterns with generic learning (GLRCSP), and differential evolution (DE). Brain graphs are constructed within a structural-functional framework, where edge weights are defined based on geometric distances and correlations. Graph's dimensionality reduction is achieved by applying physiological regions of interest (ROIs) and Kron reduction to preserve essential topological-spectral features. Feature extraction is performed using graph total variation and GLRCSP, followed by DE-based feature selection. Results:The approach was evaluated on BCI Competition III Dataset IVa and the PhysioNet eegmmidb dataset. The support vector machine with a radial basis function (SVM-RBF) classifier achieved superior performance, yielding a mean accuracy of 96.46% ± 0.81% on BCIC III-IVa. Conclusions:The proposed K-GLR-DE method demonstrates significant performance in MI-BCI classification across various training conditions, including scenarios with small and limited training sets.
Background:As the world becomes wealthier and people expect higher standards of care, the demand for healthcare services is growing rapidly. This puts significant pressure on existing medical resources and systems, making it harder to meet everyone's needs. In dermatology, for instance, the rising demand calls for creative and efficient solutions, especially in diagnosing conditions like skin cancer. Early diagnosis of skin lesions is necessary not only for effective treatment but also for providing the best possible outcomes for patients.Methods:In this paper, we present a solution using machine learning (ML) to assist in automated skin diagnosis, with a particular focus on the early detection of skin lesions, which is a key factor for effective treatment and better patient outcomes. Our method utilizes a Gaussian mixture model (GMM) with geometric features to enhance image quality by removing artifacts. We then use a color descriptor based on hybrid orthogonal combination of local binary patterns to capture the unique characteristics of the lesions. To identify the most important features for accurate classification, we apply ReliefF feature selection, prioritizing those that contribute most significantly to the results. Finally, we used several ML models such as decision tree, random forest, k-nearest neighbors, multilayer perceptron, and ensemble extra tree (ET) to classify eight different types of skin lesions effectively.Results:Remarkably, ensemble ET achieves superior performance with an accuracy of 97.31%.Conclusions:This research advances early skin lesion diagnosis, enhancing patient care in dermatology.
Background: Multiple sclerosis (MS) is an autoimmune disease of the central nervous system which is the main reason of disabilities of young adults. MS occurs when the immune system attacks the central nervous system and destroys the myelin sheaths of neurons. Loss of myelin sheaths results in appearing several lesions in different parts of the brain. The place and amount of lesions are important criteria for determining the level and progression of the disease. These parameters are usually determined manually by an expert which can be time-consuming and inaccurate. Methods: Considering the effectiveness of artificial intelligence (AI)-based methods in diagnosing and predicting different diseases, and the increasing need for driving new and effective diagnostic methods, this challenge, entitled “Diagnosing MS from magnetic resonance imaging (MRI) Images,” has been organized by Isfahan Province Elites Foundation in collaboration with Medical Image and Signal Processing Research Center of Isfahan University of Medical Sciences, as a part of Isfahan AI 2024 event, held in October 2024 in Isfahan, Iran. The challenge has been dedicated to find new AI-based methods for the segmentation and localization of lesions in MRI images of patients with MS. The challenge had three steps, where in the first and second steps, the teams received the train and test datasets, respectively. Finally, the selected teams were invited to the last round of the competition, held in person, and received the last test dataset. Results: Based on the received results, the best achieved dice score was 0.33, best sensitivity was 0.349, best precision was 0.3, and the lowest centroid distance was 53.025. In addition, the best accuracy for lesion detection in periventricular, deep white matter, juxtacortical, and infratentorial parts of the brain was 80.282%, 74%, 63.492%, and 62.5%, respectively. Conclusion: Several methods, mostly based on deep learning, have been submitted. The results show that AI has the ability for the segmentation and localization of lesions. However, the received results are still far from the desired accuracy, which shows a need for further improvement and studies in this field.
Background: The use of sedative drugs during various medical procedures is on the rise, necessitating close monitoring of respiratory function throughout the administration process. Continuous auscultation of tracheal sounds is an effective method for monitoring respiratory status. However, it requires constant attention from the operator, which may not always be feasible. Methods: This concept led to the development of a tracheal sound dataset featuring recordings from 16 patients who underwent cataract surgery at Alzahra Hospital, a university hospital in Isfahan, Iran. To ensure accuracy, the dataset was carefully examined with the assistance of an anesthesiology team, providing precise ground truth annotations for respiratory depression (RD) intervals at a resolution of one second. The Isfahan National Elite Foundation hosted the Isfahan artificial intelligence (AI) 2024 events to advance AI-based detection technologies and offered financial support for five challenges, including the competition for detecting RD from tracheal sounds. Twelve teams from various provinces across Iran participated, utilizing a shared dataset for their evaluations. Results: The teams that achieved the first through third places were Houshmandsazan, Houshava, and Hoopad, with F1-Scores of 65.18%, 50.44%, and 21.73%, respectively. All participating teams utilized deep learning techniques to detect RD intervals, achieving notable performance, yet opportunities for further improvement remain. Conclusion: This paper summarizes the performance of these teams, detailing the metrics used to assess their results and the methodologies employed by the top three competitors.
Background:Analyzing neural data such as electroencephalography (EEG) data often involves dealing with high-dimensional datasets, where not all channels provide equally meaningful information. Selecting the most relevant channels is crucial for improving computational efficiency and ensuring robust insights into neural dynamics.Method:This study introduces the Importance of Channels based on Effective Connectivity (ICEC) criterion for quantifying effective connectivity (EC) in each channel. EC refers to the causal influence one neural region exerts over another, providing insights into the directional flow of information. Using this criterion, we propose an unsupervised channel selection method that accounts for the intensity of interactions among channels.Results:To evaluate the proposed channel selection method, we applied it to three well-known EEG datasets across four categories. The assessment involved calculating the ICEC criterion using five EC metrics: partial directed coherence (PDC), generalized PDC, renormalized PDC, directed transfer function (DTF), and direct DTF. To focus on the effect of channel selection, we employed the common spatial pattern algorithm for feature extraction and a support vector machine for classification across all participants. We compared our results against other CSP-based methods. The evaluation included comparing participant-specific accuracies with and without the proposed method across five EC metrics.Conclusion:The results showed consistent improvements and a significant reduction in the number of electrodes selected for all participants. Compared to state-of-the-art methods, our approach achieved the highest accuracies: 82% (13 out of 22 channels), 86.01% (29 out of 59 channels), and 87.56% (48 out of 118 channels) across all three datasets.
Background:Celiac disease (CeD) is a chronic autoimmune condition induced by the consumption of gluten, affecting about 1.4% of the global population. The current diagnostic methods largely rely on serological testing, which may disregard certain biomarkers that are essential for an accurate diagnosis. The objective of the present investigation is to identify significant candidate biomarkers in CeD through using a bioinformatics analysis of microarray data. Methods:We analyzed three datasets of the Gene Expression Omnibus database (GSE112102, GSE113469, and GSE164883) to conduct a comprehensive bioinformatics approach. We performed a meta-analysis of differentially expressed genes (DEGs), constructed gene ontology and pathway analyses, and developed protein-protein interaction networks to identify and analyze hub genes and their associated miRNAs. Results:We detected 165 DEGs (79 upregulated and 86 downregulated). Five key hub genes - STAT1, CDC20, perforin-1, CCL2, and MYC were identified as critical regulators involved in controlling both immune system activity and cell cycle progression. Significantly, important miRNAs, including hsa-miR-155-5p, hsa-miR-145-5p, hsa-miR-18a-5p, hsa-miR-34a-5p, hsa-miR-24-3p, and hsa-miR-146a-5p, were seen to have significant interactions with these hub genes. This emphasizes their potential involvement in the pathogenesis of CeD. Conclusion:The genes identified offer potential as key biomarkers for diagnosing CeD and understanding its molecular mechanisms, creating the path for improved diagnostic and therapeutic strategies.
Background:The COVID-19 pandemic has created a critical global situation, causing widespread challenges and numerous fatalities due to severe respiratory complications. Since lung involvement is a key factor in COVID-19 diagnosis and treatment, accurate identification of infected regions in lung images is essential. Methods:We propose a multiphase segmentation method based on the level set framework to determine lunginvolved areas. The shearlet transform, a high-precision directional multiresolution transform, is employed to guide the gradient flow in the level set formulation. Additionally, the phase stretch transform (PST) is applied to enhance the contrast between infected and healthy regions, improving convergence speed during segmentation. Results:The proposed algorithm was tested on 500 lung images. The method accurately identified infected areas, enabling precise calculation of the percentage of lung involvement. The use of the shearlet transform also allowed clear delineation of ground-glass opacity boundaries. Conclusion:The proposed multiphase level set method, enhanced with shearlet and phase stretch transforms, effectively segments COVID-19-infected lung regions. This approach improves segmentation accuracy and computational efficiency, offering a reliable tool for quantitative lung involvement assessment.
Background: Alzheimer’s disease (AD) is a progressive and irreversible brain disorder, characterized by a gradual decline in cognitive and memory function, with memory loss being one of the most prominent symptoms. Accurate and early diagnosis of AD is essential for effective management and treatment. Structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) are widely utilized neuroimaging modalities for diagnosing AD due to their ability to provide complementary structural and functional insights into brain abnormalities. Methods: This study introduces a novel computer-aided diagnosis system that integrates sMRI and PET data using Fuzzy Cognitive Maps (FCM) to improve diagnostic accuracy. The research is conducted using the ADNI dataset, where preprocessing of sMRI and PET images is performed using FSL and statistical parametric mapping tools, respectively. In a key innovation, features extracted from both modalities are fused and dimensionality reduction is achieved through an Autoencoder model. The reduced feature set is then classified using FCM, Support Vector Machine, k-Nearest Neighbors, and Multilayer Perceptron. Results: The FCM-based approach demonstrates superior performance, achieving the highest accuracy of 93.71%, surpassing other classifiers tested. Conclusions: This study underscores the effectiveness of integrating FCM with multimodal neuroimaging data and highlights its potential for enhancing the early and reliable diagnosis of AD.
Background:Accurate dose calculations in radiotherapy are essential, especially in complex anatomical areas such as the nasopharynx, where heterogeneous tissue compositions can greatly influence treatment outcomes. This study assesses the accuracy of the full scatter convolution (FSC) algorithm within the TiGRT treatment planning system by comparing it to the BEAMnrc Monte Carlo (MC) simulation using a head phantom. Methods:EBT3 film was strategically placed in the nasopharyngeal region to enable direct comparisons between experimental results and those derived from the FSC and MC methods. Various metrics, including the dose difference index, two-dimensional gamma index, and horizontal and vertical dose profiles, were employed for the analysis. The heterogeneous regions were classified into bone, air, and soft-tissue components. For dosimetric evaluation, the irradiated areas were segmented into four regions based on isodose values: Field region (FR), irradiated region (IR), penumbra region (PR), and out-of-FR (OOFR). Results:The greatest computational discrepancies observed between the FSC algorithm and MC simulations in the air region of the FR were -5.12% ± 1.10% and 1.93% ± 1.45%, respectively. Notable underestimations occurred in the air and soft-tissue regions of the IR, PR, and OOFR when using the FSC algorithm, with a minimum discrepancy of -9.33% ± 5.51% and a maximum of -77.28% ± 8.19%. Conversely, doses calculated for the bone region were overestimated by 53.64% ± 5.65%. In comparison, the MC calculations in the IR region revealed discrepancies of 1.90% ± 1.55% (air), including a maximum underestimation of -8.82% ± 1.18% in the bone area within the PR. The gamma pass rates for different tissue types under local and global modes, using 3%-3 mm gamma criteria, demonstrate that the MC method consistently outperformed the TiGRT method across all tissue types, especially in the air (99.9%) and bone (99.8%) regions. Conclusions:The findings reveal that the FSC algorithm tends to underestimate doses in soft tissue and air while overestimating doses in bone. In contrast, there was excellent agreement between MC calculations and experimental measurements, highlighting the FSC algorithm's lower consistency.
