Introduction Gaussian noise is often added during the acquisition or transmission of medical images, which can blur important organs and reduce diagnostic accuracy. To address this issue, a hybrid denoising model that combines BayesShrink thresholding in the wavelet domain with Patch-Wise Local Principal Component Analysis (PLPCA) is proposed. Methods The framework initially utilises the concept of local patch redundancy by applying PLCA to effectively suppress noise while preserving finer details. The remaining noise is again decomposed using the wavelet transform, with adaptive BayesShrink thresholding applied to refine the coefficients. Subsequently, the reconstructed signal is obtained. The resultant denoised images are then combined to obtain the final enhanced image. HRCTs and MRI datasets were corrupted by Gaussian noise at σ = 10-40 and were validated. Results Quantitative analysis based on PSNR, entropy, BRISQUE, NIQE, and PIQE confirmed the consistent high impact of the proposed method across traditional, hybrid, and deep learning baselines. It is worth noting that the approach achieved PSNR values of 31.15 dB at HRCT and 33.00 dB at MRI when σ = 10, as well as at high noise levels (23.95 dB and 22.64 dB, respectively). Discussion The proposed method consistently excels over traditional, hybrid, and deep learning-based strategies, as shown by quantitative assessments using PSNR, entropy, BRISQUE, NIQE, and PIQE. Conclusion The suggested framework has the strength of suppressing Gaussian noise while retaining anatomical detail. Thus, it is an encouraging option for enhancing the quality of medical image diagnosis in clinical practice.

Multi-modal Medical Image Fusion (MMIF) is an advancing field at the intersection of medical imaging, data science, and clinical diagnostics. It aims to integrate complementary data from various imaging modalities, such as MRI, CT, and PET, into a single, diagnostically superior composite image. The limitations of unimodal imaging, such as low spatial resolution, insufficient contrast, or incomplete functional characterization, have catalyzed the development of MMIF techniques to enable enhanced visualization, precise diagnosis, and personalized therapeutic planning. This review provides a comprehensive synthesis of the MMIF landscape, categorizing methodologies into five principal domains such as spatial, frequency-based, sparse representation, deep learning, and hybrid approaches. Each technique is critically evaluated for its advantages, limitations, and applicability in clinical settings. Preprocessing, registration, fusion execution, and validation are covered in this review, along with levels of fusion pixel, feature, and decision. The study reviews prominent public databases, including TCIA, OASIS, ADNI, MIDAS, AANLIB, and DDSM, comparing their imaging modalities, disease coverage, file formats, and accessibility. The evaluation of MMIF techniques is systematically addressed, providing a framework for objective performance assessment. An experimental setup is implemented on two datasets to assess the comparative efficacy of selected MMIF techniques utilizing quantitative evaluation variables such as SSIM, entropy, spatial frequency, and mutual information. The results highlight the effectiveness of hybrid and deep learning-based approaches in maintaining both anatomical detail and functional consistency across modalities. The review explores MMIF’s real-world clinical applications, including image-guided neurosurgery, spinal planning, stereotactic radiosurgery, orthopedic implant design, and oncology diagnostics. It also provides insights into future directions, such as explainable AI, federated learning, and integration with robotic surgeries. MMIF offers immense potential yet has limitations like registration errors, computational burdens, generation of artifacts, loss of specific information, and a lack of standardized evaluation metrics. Essentially, the study provides an analytical basis for healthcare experts, scientists, and engineers aiming to develop clinically scalable MMIF systems, which will become indispensable tools for improving diagnostic accuracy, treatment planning, and patient outcomes in modern healthcare.

Introduction Differentiating brain tumors through neuroimaging is challenging due to overlapping radiological features, requiring advanced techniques and clinical correlation for accurate diagnosis. The aim of this retrospective observational monocentric study is to determine the diagnostic performance of combining perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI), and magnetic resonance spectroscopy (MRS) for MRI-based differential diagnosis of the three major classes of adult malignant intra-axial brain tumors. Principal component analysis (PCA) is applied to identify relevant imaging features, with the goal of supporting preoperative diagnosis beyond conventional MRI alone. Methods We selected 72 adult patients who underwent MRI examination, including DWI, PWI, and MRS imaging before surgery, for suspected malignant intra-axial expansive lesions (namely glioblastoma, metastasis, or primary non-Hodgkin lymphoma). The definitive histological diagnosis was obtained on post-operative specimens. Quantitative variables derived from DWI, PWI, and MRS acquisition were identified and processed using principal component analysis. The differences between groups for the most relevant parameters identified by PCA were then tested by the Kruskal-Wallis test. Results Finally, a total of 11 specimens of non-Hodgkin lymphomas, 18 specimens of single metastases, and 43 specimens of wild-type glioblastomas were gathered. CBF, CBV, MTT, ADC​​, and lipid-lactate (Lip-Lac) at MRS ​​were found to be the most relevant variables for differential diagnostic purposes through PCA analysis. In particular, ADC and Lip-Lac were more strongly associated with differentiating lymphoma from the other two disease classes, while CBF, CBV, and MTT contributed more to differentiating glioblastoma from metastasis. Discussion In this study, ADC and Lip-Lac differentiated CNS lymphoma, while CBV, CBF, and MTT distinguished GBM from metastases, supporting PCA’s clinical value beyond diagnostic workflows. Conclusion The combined use of PWI, DWI, and MRS can assist the radiologist in accurate preoperative differential diagnosis of the three main classes of adult malignant intra-axial brain neoplasms, enhancing diagnostic performance beyond that of conventional MRI alone.

Purpose This study aimed to investigate multivariate regional patterns for schizophrenia (SZ) classification, sex differences, and brain age by utilizing structural MRI, demographics, and explainable artificial intelligence (AI). Methods Various AI models were employed, and the outperforming model was identified for SZ classification, sex differences, and brain age predictions. For the SZ and sex classification tasks, support vector classifier (SVC), k-nearest neighbor (KNN), and deep learning neural network (DL) models were compared. In the case of regression-based brain age prediction, Lasso regression (LR), Ridge regression (RR), support vector regression (SVR), and DL models were compared. For each regression or classification task, the optimal model was further integrated with the Shapley additive explanations (SHAP), and significant multivariate brain regional patterns were identified. Results Our results demonstrated that the DL model outperformed other models in SZ classification, sex differences, and brain age predictions. We then integrated outperforming DL model with SHAP, and this integrated DL-SHAP model was used to identify the individualized multivariate regional patterns associated with each prediction. Using the DL-SHAP approach, we found that individuals with SZ had anatomical changes, particularly in the left pallidum, left posterior insula, left hippocampus, and left putamen regions, and such changes associated with SZ were different between female and male patients. Finally, we further applied the DL-SHAP method to brain age prediction and suggested important brain regions related to aging in health controls (HC) and SZ processes. Conclusion This study systematically utilized predictive modeling and novel explainable AI approaches and identified the complex multivariate brain regions involved with SZ classification, sex differences, and brain aging, thereby building a deeper understanding of neurobiological mechanisms involved in the disease, offering new insights into future SZ diagnosis and treatments, and laying the foundation for the development of precision medicine.

Aim The aim of this study is to determine the most prevalent types of federated learning, discuss their uses in healthcare, highlight the most significant issues, and suggest methods for further research. Context When it comes to handling distributed data, federated learning is revolutionary, especially in sensitive sectors like healthcare. In order to improve the outcomes of the growing number of healthcare studies, there must be a method to safely and effectively analyze and use this enormous data. Objective The purpose of this research is to use a large corpus of 6,800 healthcare studies published between 2000 and 2024 and apply topic modeling using Latent Semantic Analysis (LSA). Methods The corpus was analyzed using LSA with the goal of identifying latent themes that capture the spirit of federated learning in the healthcare industry. In order to provide an organized overview of the subject matter, a five-topic solution was devised. To guarantee relevance and clarity, the topics' coherence was assessed. Results The term frequency and the inverse document frequency of high-loading terms provided five major topic solutions. The coherence score of the five-topic solution was achieved, i.e., 0.789, indicating a high level of relevance and integration among the identified topics. Different types of federated learning (FL), applications of FL, and the key challenges and the possible solution associated with FL have been analyzed. Conclusion This study highlights the significance of using FL to improve privacy-preserving data analysis in the healthcare field, which may lead to the development of creative solutions for complex problems.

Background A thyroglossal duct cyst (TGDC) is a common congenital anomaly. However, the development of carcinoma within it is rare. Submental presentation and the concomitant TGDC carcinoma with thyroid gland carcinoma are indeed very rare. Case Presentation In this case report, a TGDC carcinoma with concomitant thyroid carcinoma in a fifty year-old Iraqi middle aged female presented with a submental mass. It was diagnosed initially as a sublingual ranula. Clinical examination showed a non-mobile tender hard mass at the submental region with no obvious thyroid gland enlargement. An imaging study showed a normal thyroid size and texture with a complex cystic-solid lesion involving the sublingual space. Fine needle aspiration cytology showed atypical follicular epithelial inflammatory cells within the thyroid nodule. Conclusion The neck mass smear suggested papillary thyroid carcinoma, which was confirmed on surgery by Sistrunk procedure and postoperative histopathology. Subsequently, the patient was kept on radioactive iodine therapy. Papillary thyroid carcinoma arising in TGDC may present as a large complex midline mass at the upper neck or floor of the mouth and should be kept in mind even if there is no history of thyroglossal duct cyst or a history of thyroid nodule.

Background Moyamoya disease (MMD) is an occlusive cerebrovascular condition characterized by progressive stenosis of the terminal portion of the internal carotid artery (ICA) and the development of an abnormal vascular network at the base of the brain. This disease predominantly affects individuals in East Asian countries, with an incidence rate ranging from 6.03 to 9.1 per 100,000 people. Case Presentation We report the case of a 41-year-old Hispanic woman who presented severe headaches, nausea, vomiting, and intermittent loss of alertness over a 15-day period. Upon admission, her vital signs were normal, and no focal neurological deficits were observed. Initial plain CT imaging revealed an interhemispheric subarachnoid hemorrhage with intraventricular involvement in the occipital recess and right atrium. Subsequent angiographic CT with 3D reconstructions exhibited the classic 'puff of smoke' appearance indicative of Moyamoya disease. Perfusion-weighted imaging (PWI) demonstrated normal relative cerebral blood flow, blood volume, and mean transit time in both hemispheres. Based on these imaging findings, the patient was diagnosed with MMD. She underwent an indirect revascularization procedure known as encephaloduroarteriosynangiosis, which involved suturing branches of the superficial temporal artery to the dura. Discussion This case report underscores an atypical presentation of MMD in a Hispanic patient diagnosed by a combination of digital subtraction angiography (DSA), 3D CT angiography, and brain perfusion MRI. The findings highlight the importance of recognizing and diagnosing this rare condition in populations outside of East Asia. Furthermore, this report includes a review of the updated literature on MMD, providing valuable information on its diagnosis and management. Conclusion The clinical presentation and imaging findings, in this case, underscore the need for advanced diagnostic techniques, such as perfusion-weighted imaging (PWI) and quantitative color-coded parametric DSA (QDSA), to improve diagnostic precision and treatment planning. The successful application of indirect revascularization through encephaloduroarteriosynangiosis demonstrates the efficacy of surgical interventions in the treatment of MMD. Addressing ethnic disparities in MMD is crucial to improving early diagnosis and patient outcomes. Future research should focus on refining treatment algorithms, investigating nonsurgical interventions, and examining cognitive and psychological outcomes to further improve patient care.

Background Recent studies have identified links between schizophrenia and abnormalities in the brain's salience network, a crucial system with primary hubs in the anterior insula and anterior cingulate cortex. This network is divided into two subsystems: the dorsal salience network, which processes sensory information and allocates attention to self-generated or external sensory stimuli, and the ventral salience network, associated with processing the emotional valence of stimuli sensations. Methods This pilot study analyzed the resting state functional magnetic resonance imaging (fMRI) data from 14 schizophrenia patients and 16 healthy controls. We focused on the functional connectivity within the salience network's dorsal and ventral subsystems, particularly between the dorsal anterior insula and frontoparietal areas, and the ventral anterior insula and the subgenual anterior cingulate cortex. Results The analysis revealed that schizophrenia patients displayed weaker connectivity within the dorsal salience network, notably between the dorsal anterior insula and frontoparietal areas. In contrast, these patients demonstrated increased connectivity within the ventral salience system, especially between the ventral anterior insula and the subgenual anterior cingulate cortex. Conclusion Our findings highlight that disruptions in the salience network in schizophrenia vary depending on the type of information being processed. This variance underscores the complexity of the disorder and the specific challenges it poses to the brain's ability to process and prioritize information.

Background: Due to the complexities of scrutinizing and diagnosing brain tumors from MR images, brain tumor analysis has become one of the most indispensable concerns. Characterization of a brain tumor before any treatment, such as radiotherapy, requires decisive treatment planning and accurate implementation. As a result, early detection of brain tumors is imperative for better clinical outcomes and subsequent patient survival. Introduction: Brain tumor segmentation is a crucial task in medical image analysis. Because of tumor heterogeneity and varied intensity patterns, manual segmentation takes a long time, limiting the use of accurate quantitative interventions in clinical practice. Automated computer-based brain tumor image processing has become more valuable with technological advancement. With various imaging and statistical analysis tools, deep learning algorithms offer a viable option to enable health care practitioners to rule out the disease and estimate the growth. Methods: This article presents a comprehensive evaluation of conventional machine learning models as well as evolving deep learning techniques for brain tumor segmentation and classification. Conclusion: In this manuscript, a hierarchical review has been presented for brain tumor segmentation and detection. It is found that the segmentation methods hold a wide margin of improvement in the context of the implementation of adaptive thresholding and segmentation methods, the feature training and mapping requires redundancy correction, the input data training needs to be more exhaustive and the detection algorithms are required to be robust in terms of handling online input data analysis/tumor detection.

Background: Dystrophin strengthens muscle cells; however, in muscular dystrophy, dystrophin is deficient due to an abnormal sugar chain. This abnormality occurs in skeletal muscle and in brain tissue. Objective: This study aimed to non-invasively analyze the neural organization of the brain in muscular dystrophy. We used a mouse model of muscular dystrophy to study whether changes in brain structure and neurodegeneration following dystrophin deficiency can be assessed by 7T magnetic resonance imaging. Methods: C57BL/10-mdx (X chromosome-linked muscular dystrophy) mice were used as the dystrophic mouse model and healthy mice were used as controls. Ventricular enlargement is one of the most common brain malformations in dystrophin-deficient patients. Therefore, we examined whether ventricular enlargement was observed in C57BL/10-mdx using transverse-relaxation weighted images. Brain parenchyma analysis was performed using diffusion MRI with diffusion tensor images and neurite orientation dispersion and density imaging. Parenchymal degeneration was assessed in terms of directional diffusion, nerve fiber diffusion, and dendritic scattering density. Results: For the volume of brain ventricles analyzed by T2WI, the average size was 1.5 times larger in mdx mice compared to control mice. In the brain parenchyma, a significant difference (p < 0.05) was observed in parameters indicating disturbances in the direction of nerve fibers and dendritic scattering density in the white matter region. Conclusion: Our results show that changes in brain structure due to dystrophin deficiency can be assessed in detail without tissue destruction by combining diffusion tensor images and neurite orientation dispersion and density imaging analyses.