Search Results (2,232)

Amidrapid advances in intelligent medicine, decoding brain activity from electroencephalogram (EEG) signals has emerged as a critical technical frontier for brain–computer interfaces and medical AI systems. Given the inherent spatial resolution limitations of an EEG, researchers frequently integrate functional magnetic resonance imaging (fMRI) to enhance neural activity representation. However, fMRI acquisition is inherently complex. Consequently, efforts increasingly focus on cross-modal transformation methods that map EEG signals to fMRI data, thereby extending EEG applications in neural mechanism studies. The central challenge remains generating high-fidelity fMRI images from EEG signals. To address this, we propose a diffusion model-based framework for cross-modal EEG-to-fMRI generation. To address pronounced noise contamination in electroencephalographic (EEG) signals acquired via simultaneous recording systems and temporal misalignments between EEGs and functional magnetic resonance imaging (fMRI), we first apply Fourier transforms to EEG signals and perform dimensionality expansion. This constructs a spatiotemporally aligned EEG–fMRI paired dataset. Building on this foundation, we design an EEG encoder integrating a multi-layer recursive spectral attention mechanism with a residual architecture.In response to the limited dynamic mapping capabilities and suboptimal image quality prevalent in existing cross-modal generation research, we propose a diffusion-model-driven EEG-to-fMRI generation algorithm. This framework unifies the EEG feature encoder and a cross-modal interaction module within an end-to-end denoising U-Net architecture. By leveraging the diffusion process, EEG-derived features serve as conditional priors to guide fMRI reconstruction, enabling high-fidelity cross-modal image generation. Empirical evaluations on the resting-state NODDI dataset and the task-based XP-2 dataset demonstrate that our EEG encoder significantly enhances cross-modal representational congruence, providing robust semantic features for fMRI synthesis. Furthermore, the proposed cross-modal generative model achieves marked improvements in structural similarity, the root mean square error, and the peak signal-to-noise ratio in generated fMRI images, effectively resolving the nonlinear mapping challenge inherent in EEG–fMRI data. Full article

Background/Objectives: The cerebellum contributes to both motor and cognitive functions. As basketball requires the integration of these abilities, basketball athletes provide an ideal model for exploring cerebellar adaptations. This study aimed to examine multidimensional cerebellar adaptations in basketball athletes and their associations with physical performance. Methods: In this study, 55 high-level basketball athletes and 55 non-athletes matched for age and gender were recruited for multimodal magnetic resonance imaging data collection and physical fitness tests. We compared the structural and functional differences in the brain between the two groups and analyzed the correlations between regional brain indices and physical fitness test outcomes. Results: Basketball athletes exhibited increased gray matter volume in Crus I, alongside heightened ALFF signal in Crus I and improved regional homogeneity in Crus II and VII b compared to non-athletes. Diffusion kurtosis imaging analysis demonstrated that athletes perform elevated kurtosis fractional anisotropy and decreased radial kurtosis within the cerebellar cortex and peduncles, with cortical modifications mainly localized around Crus I and lobule VI. Notably, both kurtosis fractional anisotropy and the amplitude of low-frequency fluctuations displayed positive correlations with vertical jump performance, an indicator specific to basketball ability. Conclusions: Basketball athletes exhibit structural, microstructural, and functional cerebellar adaptations, especially in Crus I. These modifications involve regions associated with motor and cognitive representations within the cerebellum, and part of the indexes are linked to the athletes’ physical performance. This study enhances our understanding of cerebellar adaptive changes in athletes, providing new insights for future research aimed at fully elucidating the role of the cerebellum in these individuals. Full article

Background: Locally advanced cervical cancer (LACC) represents a significant challenge in oncology, requiring accurate assessment of local extent and metastatic spread. Multiparametric magnetic resonance imaging (mpMRI) has assumed a central role in the loco-regional characterization of the tumor due to its high soft-tissue resolution and the ability to integrate functional information. Objectives: In this narrative review, we explore the use of mpMRI in the diagnosis, staging, and treatment response of LACC, comparing its performance with that of PET/CT, which remains complementary for remote staging. The potential of whole-body magnetic resonance imaging (WB-MRI) and hybrid PET/MRI techniques is also analyzed, as well as the emerging applications of radiomics and artificial intelligence. The paper also discusses technical limitations, interpretative variability, and the importance of protocol standardization. The goal is to provide an updated and translational summary of imaging in LACC, with implications for clinical practice and future research. Methods: Prospective and retrospective studies, systematic reviews, and meta-analyses on adult patients with cervical cancer were included. Results: Fifty-two studies were included. MRI demonstrated a sensitivity and specificity greater than 80% for parametrial and bladder invasion, but limited sensitivity (45–60%) for lymph node disease, lower than PET/CT. Multiparametric MRI was useful in early prediction of response to chemotherapy and radiotherapy and in distinguishing residual disease from fibrosis. The integration of MRI into Image-Guided Adaptive Brachytherapy (IGABT) resulted in improved oncological outcomes and reduced toxicity. The applications of radiomics and AI demonstrated enormous potential in predicting therapeutic response and lymph node status in the MRI study, but multicenter validation is still needed. Conclusions: MRI is the cornerstone of the local–regional staging of advanced cervical cancer; it has become an essential and crucial tool in treatment planning. Its use, combined with PET/CT for lymph node assessment and metastatic disease staging, is now the standard of care. Future prospects include the use of whole-body MRI and the development of predictive models based on radiomics and artificial intelligence. Full article

Intraoperative ultrasound (IOUS) has developed from a rudimentary adjunct into a versatile modality that now plays a crucial role in neurosurgery. Offering real-time, radiation-free and repeatable imaging at the surgical site, it provides distinct advantages over intraoperative magnetic resonance (MRI) and computed tomography (CT) in terms of accessibility, workflow integration and cost. The clinical spectrum of IOUS is broad: in cranial surgery it enhances the extent of resection of gliomas and metastases, supports dissection in meningiomas and enables localization of MRI-negative pituitary adenomas; in spinal surgery, it guides resection of intradural and intramedullary tumors, assists in myelotomy planning and confirms decompression in degenerative conditions such as cervical myelopathy and ossification of the posterior longitudinal ligament. IOUS also offers unique insights into cerebrospinal fluid disorders, including arachnoid webs, cysts, syringomyelia and Chiari malformation, where it visualizes cord compression and CSF flow restoration. In trauma and oncological emergencies, it provides immediate confirmation of decompression, directly influencing surgical decisions. Recent innovations, including contrast-enhanced ultrasound, elastography, three-dimensional navigated systems and experimental integration with artificial intelligence and robotics, are extending its functional scope. Despite heterogeneity of evidence and operator dependence, IOUS is steadily transitioning from an adjunctive tool to a cornerstone of multimodal intraoperative imaging, bridging precision, accessibility and innovation in contemporary neurosurgical practice. Full article

Background/Purpose: Skilled motor performance depends on the action–observation networks (AONs), which supports the internal simulation of perceived movements. While expertise effects are well-documented in sports, neuroimaging evidence in golf is scarce, particularly on temporal dynamics across swing phases. This study examines how golf expertise modulates AON activation and functional connectivity during temporally distinct swing phases (pre-hitting vs. hitting) and assesses implications for predictive-coding models of motor skill. Methods: Fifty-seven participants (elite golfers: n = 28; controls: n = 29) underwent functional magnetic resonance imaging (fMRI) scanning while viewing golf swing videos segmented into pre-hitting and hitting phases. Data analysis employed generalized linear models (GLMs) with two-sample t-tests for group comparisons and generalized psychophysiological interaction (gPPI) to assess functional connectivity using GLM-identified activation clusters as seeds. Results: (1) Compared to controls, elite golfers showed stronger activation in right insula and posterior cingulate cortex during pre-hitting, and in right cerebellum and bilateral postcentral cortex during hitting phases. The hitting > pre-hitting contrast revealed enhanced bilateral postcentral gyrus activation in golfers. (2) gPPI analysis demonstrated significant group × phase interaction in functional connectivity between right postcentral gyrus and left precuneus. Conclusions: Elite golf expertise dynamically retunes AON across swing phases, shifting from anticipatory interoceptive processing to impact-centered sensorimotor–parietal circuitry. These findings refine predictive-coding models of motor skill and identify the postcentral–precuneus loop as a potential target for neurofeedback interventions aimed at optimizing golf performance. Full article

Magnetic Particle Imaging (MPI) is a new type of tracer-based imaging that has great spatial and temporal resolution, does not require ionizing radiation, and can see deep into tissues by directly measuring the nonlinear magnetization response of superparamagnetic iron oxide nanoparticles (SPIONs). Unlike Magnetic Resonance Imaging (MRI) or Computed Tomography (CT), MPI has very high contrast and quantitative accuracy, which makes it perfect for use in dynamic cardiovascular applications. This study presents a full picture of the most recent changes in cardiac MPI, such as the physics behind Field-Free Point (FFP) and Field-Free Line (FFL) encoding, new ideas for tracer design, and important steps in the evolution of scanner hardware. We discuss the clinical relevance of cardiac MPI in visualizing myocardial perfusion, quantifying blood flow, and guiding real-time interventions. A hybrid imaging workflow, which improves anatomical detail and functional assessment, is utilized to explore the integration of MPI with complementary modalities, particularly MRI. By consolidating recent preclinical breakthroughs and highlighting the roadmap toward human-scale implementation, this article underscores the transformative potential of MPI in cardiac diagnostics and image-guided therapy. Full article

Biomedical images, whether acquired by techniques such as magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, X-ray, or other methods, are commonly obtained and permanently stored for diagnostic purposes. Therefore, leveraging this large number of images has become essential for the development of intelligent medical diagnostic systems. In this work, we propose a new biomedical image recognition in two stages: the first stage is to introduce a new image feature extraction technique using quaternion Legendre orthogonal moments (QLOMs) to extract features from biomedical images. The second stage is to use radial basis function (RBF) neural networks for image classification to know the type of disease. To evaluate our computer-aided medical diagnosis system, we present a series of experiments were conducted. Based on the results of a comparative study with recent approaches, we conclude that our method is very promising for the detection and recognition of dangerous diseases. Full article

Background: This retrospective cross-sectional study evaluated the association of the global wall thickness index (GTI), derived from cardiovascular magnetic resonance (CMR), with demographic, clinical, and imaging findings, as well as heart failure history in transfusion-dependent thalassemia (TDT) patients. Methods: We analyzed 1154 TDT patients (52.9% female, 37.46 ± 10.67 years) from the Extension-Myocardial Iron Overload in Thalassemia project and 167 healthy controls (54.5% female, 36.33 ± 15.78 years). The CMR protocol included the T2* technique for the assessment of iron overload, cine imaging for the assessment of left ventricular (LV) function and size, and late gadolinium enhancement (LGE) imaging for the detection of replacement myocardial fibrosis (in the subset of 366 patients who underwent contrast administration). GTI (in mm/m²) was calculated from LV mass and end-diastolic volume. Results: GTI discriminated TDT patients from controls better than the LV end-diastolic volume index. Among TDT patients, GTI was higher in males, in those with diabetes, and in those with severe myocardial iron overload (cardiac T2* < 10 ms), but was unrelated to age, hemoglobin and ferritin levels, splenectomy, hepatic and pancreatic T2* values, LV ejection fraction, and fibrosis. GTI showed a diagnostic performance comparable to global heart T2* and superior to LV ejection fraction in identifying patients with prior heart failure. Conclusions: GTI is elevated in TDT patients compared with healthy controls. Male sex and severe myocardial iron overload are key determinants of GTI in TDT. Increased GTI is linked to a history of heart failure, supporting its role as a complementary tool to conventional CMR indices. Full article

The advent of nanotechnology has revolutionized the field of medicine, particularly in the development of targeted therapeutic strategies. Among these, radiolabeled nanomaterials have emerged as promising tools for both diagnostic and therapeutic applications, offering precise delivery of radiation to diseased tissues while minimizing damage to healthy ones. Notably, Lutetium-177 (¹⁷⁷Lu) has gained significant attention due to its favorable emission properties and availability that render it suitable for imaging and therapeutic purposes. When integrated with magnetic nano-formulations, ¹⁷⁷Lu-labeled systems combine the benefits of targeted radiation therapy (TRT) with the unique properties of magnetic nanoparticles (MNPs), such as magnetic resonance imaging (MRI) contrast enhancement and magnetically guided drug delivery to address challenges in diagnosis and treatment of diseases, such as cancer. By examining the latest advancements in their design, particularly surface functionalization and bioconjugation strategies, this study aims to highlight their efficacy in targeted therapy, imaging, and theranostic applications. Furthermore, we discuss the current challenges, such as scalability, biocompatibility, and regulatory hurdles, while proposing future directions to enhance their clinical translation. This comprehensive review underscores the transformative potential of ¹⁷⁷Lu-labeled magnetic nano-formulations in precision medicine and their role in shaping the future of therapeutic interventions. Full article

Background: Desmoid tumors (DTs) are rare, locally aggressive soft-tissue neoplasms that often affect women of reproductive age. Pregnancy and prior abdominal surgery or trauma have been associated with tumor development and growth, while imaging frequently overlaps with abdominal-wall endometriosis. We present the case of a 39-year-old woman with an abdominal-wall DT and provide a narrative review of the literature focused on pregnancy/postpartum patterns, differential diagnosis, and management. Methods: A narrative review of PubMed/MEDLINE and Web of Science (January 1982–December 2024) was conducted. We included English-language case reports/series, narrative/descriptive reviews, and consensus statements relevant to DTs in pregnancy or reproductive-age women, emphasizing abdominal-wall disease. Results: The patient’s right abdominal-wall mass enlarged during pregnancy and further post-partum imaging repeatedly suggested endometriosis. En bloc resection revealed desmoid-type fibromatosis composed of bland spindle cells in a collagenous stroma, with nuclear β-catenin and lymphoid enhancer–binding factor 1 (LEF1) positivity on immunohistochemistry. Magnetic resonance imaging (MRI) at 12 months showed no recurrence. Across included studies, pregnancy and post-partum enlargement is common, abdominal-wall DTs frequently mimic scar endometriosis, and pre-operative ultrasound has limited specificity. Current practice supports watch-and-wait for stable, asymptomatic lesions and function-preserving surgery for symptomatic progression, while systemic options (anti-estrogens, low-dose chemotherapy, and tyrosine kinase inhibitors) are reserved for progressive or unresectable disease. Recurrence risk relates to age, size, site, and β-catenin status; future pregnancy is not contraindicated. Conclusions: Abdominal-wall DTs, although rare, should be considered in the differential diagnosis of reproductive-age women presenting with abdominal-wall masses, particularly during or after pregnancy. Full article

Primary bone tumors encompass a heterogeneous spectrum ranging from benign entities to highly aggressive sarcomas. This review aims to summarize the current role and future perspectives of nuclear medicine in the diagnosis, staging, and management of primary bone tumors. Accurate diagnosis and staging are critical yet challenging due to histologic heterogeneity and overlapping imaging features. While radiographs, computed tomography (CT), and magnetic resonance imaging (MRI) remain essential, nuclear medicine provides a complementary functional perspective by assessing bone turnover, vascularity, and glucose metabolism. Bone scintigraphy is highly sensitive for skeletal lesions and useful for detecting skip lesions or multifocal disease, although its specificity is limited. Hybrid single-photon emission computed tomography (SPECT)/CT enhances diagnostic confidence through precise anatomic localization and quantitation. [¹⁸F]fluorodeoxyglucose ([¹⁸F]FDG) positron emission tomography (PET)/CT, by directly visualizing tumor metabolism, has become a cornerstone in osteosarcoma and Ewing sarcoma management, demonstrating superiority over bone scintigraphy for detecting skeletal metastases. In chondrosarcoma, [¹⁸F]FDG uptake correlates with histologic grade, although overlap with benign cartilage tumors complicates interpretation. Future directions include the integration of quantitative SPECT, artificial intelligence, and novel tracers such as [¹⁸F]sodium fluoride and [⁶⁸Ga]Ga-fibroblast activation protein inhibitor (FAPI). Collectively, nuclear medicine imaging is becoming a key element in musculoskeletal oncology, offering unique biological insights that complement anatomic imaging and contribute to improved patient management. Full article

Primary tricuspid regurgitation (TR) is an underrecognized valve disease characterized by structural abnormalities of the tricuspid valve (TV) apparatus, including leaflet prolapse, flail, rheumatic degeneration, carcinoid involvement and congenital malformations such as Ebstein’s anomaly. Historically neglected and often misclassified as functional, primary TR has recently gained attention due to advances in multimodality imaging and increased awareness of its pathophysiological complexity and adverse outcomes. A major challenge that remains is the accurate diagnosis of primary TR, as well as the optimal timing for intervention, particularly in asymptomatic patients. While surgical repair or replacement has been the traditional approach, recent developments in transcatheter therapies, such as tricuspid edge-to-edge repair, have broadened the therapeutic landscape for patients considered at high surgical risk. In this context, personalized medicine has emerged as a central paradigm in the management of this valvular disease. Tailored therapeutic decisions should include anatomical, functional, and clinical parameters, as well as patient-specific risk factors such as age and comorbidities. Advanced imaging modalities, including 3D echocardiography and cardiac magnetic resonance, are essential for guiding this individualized approach. This review summarizes the current understanding of the etiology, pathophysiology, diagnostic tools, and treatment strategies for primary TR, highlighting the critical role of personalized treatment pathways in optimizing clinical outcomes. Full article

Humans show neural specificity in processing animal-related information, especially regarding companion animals. However, the underlying cognitive mechanisms remain poorly understood. This study’s main objective is to investigate human neural specificity in processing companion animal-related information, compared to other animal types and inanimate objects. Forty participants viewed four image types (companion animals, neutral animals, positive objects, neutral objects) during functional magnetic resonance imaging (fMRI) scans and judged image categories. T-test results showed: 1. Processing companion animal-related information elicited specific brain activation in the right Inferior Parietal Lobe (right IPL), right Middle Occipital Gyrus (right MOG), left Superior Frontal Gyrus (left SFG), and left Precuneus (left PCu) (<0.05). 2. Generalized Psychophysiological Interaction (gPPI) analysis revealed specific functional connectivity changes between relevant brain regions during companion animal info processing (<0.05). 3. Dynamic Causal Modelling (DCM) analysis showed significant intrinsic connectivity differences between pet owners and non-pet owners: specifically, left IPL to left PCu and right ACC to right MOG (posterior probability, Pp > 0.95). The results of this study demonstrate that humans exhibit distinct neural specificity when processing information related to companion animals compared with livestock and inanimate objects. This neural specificity involves brain regions linked to higher-order cognitive functions (e.g., visual processing, emotion, and attachment), all of which are integral components of the human attachment network. These regions are part of the human attachment network, and their functional role likely relates to attachment mechanisms. These findings help clarify companion animals’ impact on human neural activity during human–animal interactions and guide applications like animal-assisted therapy. Full article

Concussions, classified as a type of mild traumatic brain injury (mTBI), are frequently underdiagnosed due to the subjective nature of symptoms and limitations in existing diagnostic methodologies. Current clinical evaluations, including tools such as the Sport Concussion Assessment Tool 5 (SCAT5), Balance Error Scoring System (BESS), and Vestibular Ocular Motor Screening (VOMS), demonstrate high sensitivity and specificity but often fail to capture the full complexity of concussive injuries. Emerging diagnostic approaches, such as blood biomarkers (for example, glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), S100 calcium-binding protein B (S100B), and tau) and advanced neuroimaging techniques (for example, diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI)), show promise but remain impractical for routine clinical use due to accessibility and standardization challenges. This review examines objective markers, including neuroimaging, electrophysiological measures (for example, Electroencephalography (EEG), Magnetoencephalography (MEG)), and real-time diagnostic tools, as complementary strategies to enhance traditional clinical evaluations. Findings indicate that while clinical assessments remain central to concussion diagnosis, integrating them with advanced imaging and electrophysiological tools can provide more accurate diagnostics and recovery tracking. Biomarkers, although not yet ready for widespread use, hold significant potential for future applications. Further research is required to validate these methods and establish standardized protocols to facilitate their integration into clinical practice. Full article

Background: Current fMRI (functional magnetic resonance imaging)-driven brain information decoding for visual image reconstruction techniques faces issues such as poor structural fidelity, inadequate model generalization, and unnatural visual image reconstruction in complex scenarios. Methods: To address these challenges, this study proposes a Dynamic Confidence Bayesian Adaptive Network (DCBAN). In this network model, deep nested Singular Value Decomposition is introduced to embed low-rank constraints into the deep learning model layers for fine-grained feature extraction, thus improving structural fidelity. The proposed Bayesian Adaptive Fractional Ridge Regression module, based on singular value space, dynamically adjusts the regularization parameters, significantly enhancing the decoder’s generalization ability under complex stimulus conditions. The constructed Dynamic Confidence Adaptive Diffusion Model module incorporates a confidence network and time decay strategy, dynamically adjusting the semantic injection strength during the generation phase, further enhancing the details and naturalness of the generated images. Results: The proposed DCBAN method is applied to the NSD, outperforming state-of-the-art methods by 8.41%, 0.6%, and 4.8% in PixCorr (0.361), Incep (96.0%), and CLIP (97.8%), respectively, achieving the current best performance in both structural and semantic fMRI visual image reconstruction. Conclusions: The DCBAN proposed in this thesis offers a novel solution for reconstructing visual images from fMRI signals, significantly enhancing the robustness and generative quality of the reconstructed images. Full article