Search Results (336)

Tissue-engineered scaffolds, particularly composite scaffolds composed of polymers combined with ceramics, bioactive glasses, or nanomaterials, play a vital role in regenerative medicine by providing structural and biological support for tissue repair. As scaffold designs grow increasingly complex, the need for non-invasive imaging modalities capable of monitoring scaffold integration, degradation, and tissue regeneration in real-time has become critical. This review summarizes current non-invasive imaging techniques used to evaluate tissue-engineered constructs, including optical methods such as near-infrared fluorescence imaging (NIR), optical coherence tomography (OCT), and photoacoustic imaging (PAI); magnetic resonance imaging (MRI); X-ray-based approaches like computed tomography (CT); and ultrasound-based modalities. It discusses the unique advantages and limitations of each modality. Finally, the review identifies major challenges—including limited imaging depth, resolution trade-offs, and regulatory hurdles—and proposes future directions to enhance translational readiness and clinical adoption of imaging-guided tissue engineering (TE). Emerging prospects such as multimodal platforms and artificial intelligence (AI) assisted image analysis hold promise for improving precision, scalability, and clinical relevance in scaffold monitoring. Full article

Mammary Paget disease (MPD) is a rare cutaneous malignancy associated with underlying ductal carcinoma in situ (DCIS) or invasive ductal carcinoma (IDC). Clinically, it appears as eczematous changes in the nipple and areola complex (NAC), which may include itching, redness, crusting, and ulceration; these symptoms can sometimes mimic benign dermatologic conditions such as nipple eczema, making early diagnosis challenging. A 56-year-old woman presented with persistent erythema and scaling of the left nipple, which did not respond to conventional dermatologic treatments: a high degree of suspicion prompted further investigation. Reflectance confocal microscopy (RCM) revealed atypical, enlarged epidermal cells with irregular boundaries, while line-field confocal–optical coherence tomography (LC-OCT) demonstrated thickening of the epidermis, hypo-reflective vacuous spaces and abnormally large round cells (Paget cells). These non-invasive imaging findings were consistent with an aggressive case of Paget disease despite the absence of clear mammographic evidence of underlying carcinoma: in fact, several biopsies were needed, and at the end, massive surgery was necessary. Non-invasive imaging techniques, such as dermoscopy, RCM, and LC-OCT, offer a valuable diagnostic tool in detecting Paget disease, especially in early stages and atypical forms. Full article

We present a rare case of delayed retrobulbar and adrenal metastases from renal cell carcinoma (RCC), diagnosed 5.5 years after radical nephrectomy. The patient exhibited symptomatic orbital involvement, with imaging revealing a hypervascular retrobulbar mass and an incidental right adrenal lesion, indicative of an oligometastatic state. Owing to the patient’s refusal of surgical resection, stereotactic ablative radiotherapy (SABR) was delivered to the retrobulbar lesion at a total dose of 40 Gy in five fractions, concurrently with immune checkpoint inhibitor therapy. Treatment planning prioritized sparing adjacent critical structures, including the optic chiasm and brainstem. Follow-up over 4 years demonstrated sustained radiologic stability and volume reduction in both metastatic lesions without evidence of progression. This case underscores the potential efficacy of SABR in achieving durable local control of RCC metastases, particularly in anatomically constrained regions where surgery is unfeasible. Moreover, it highlights the value of a multidisciplinary, multimodal treatment approach incorporating advanced radiotherapy techniques and systemic immunotherapy. Lastly, it reinforces the importance of prolonged surveillance in RCC survivors due to the potential for late metastatic recurrence at uncommon sites. Full article

Coronary plaque vulnerability, more than luminal stenosis, drives acute coronary syndromes. Optical coherence tomography (OCT), intravascular ultrasound (IVUS), and coronary computed tomography angiography (CCTA) visualize plaque morphology in vivo, but manual interpretation is time-consuming and operator-dependent. We performed a narrative literature survey of artificial intelligence (AI) applications—focusing on machine learning (ML) architectures—for automated coronary plaque segmentation and risk characterization across OCT, IVUS, and CCTA. Recent ML models achieve expert-level lumen and plaque segmentation, reliably detecting features linked to vulnerability such as a lipid-rich necrotic core, calcification, positive remodelling, and a napkin-ring sign. Integrative radiomic and multimodal frameworks further improve prognostic stratification for major adverse cardiac events. Nonetheless, progress is constrained by small, single-centre datasets, heterogeneous validation metrics, and limited model interpretability. AI-enhanced plaque assessment offers rapid, reproducible, and comprehensive coronary imaging analysis. Future work should prioritize large multicentre repositories, explainable architectures, and prospective outcome-oriented validation to enable routine clinical adoption. Full article

Accurate feature matching between optical and synthetic aperture radar (SAR) images remains a significant challenge in remote sensing due to substantial modality discrepancies in texture, intensity, and geometric structure. In this study, we proposed an attention-context-aware deep learning framework (ACAMatch) for robust and efficient optical–SAR image registration. The proposed method integrates a structure-enhanced feature extractor, RS2FNet, which combines dual-stage Res2Net modules with a bi-level routing attention mechanism to capture multi-scale local textures and global structural semantics. A context-aware matching module refines correspondences through self- and cross-attention, coupled with a confidence-driven early-exit pruning strategy to reduce computational cost while maintaining accuracy. Additionally, a match-aware multi-task loss function jointly enforces spatial consistency, affine invariance, and structural coherence for end-to-end optimization. Experiments on public datasets (SEN1-2 and WHU-OPT-SAR) and a self-collected Gaofen (GF) dataset demonstrated that ACAMatch significantly outperformed existing state-of-the-art methods in terms of the number of correct matches, matching accuracy, and inference speed, especially under challenging conditions such as resolution differences and severe structural distortions. These results indicate the effectiveness and generalizability of the proposed approach for multimodal image registration, making ACAMatch a promising solution for remote sensing applications such as change detection and multi-sensor data fusion. Full article

Background/Objectives: To investigate the role of multimodal imaging, specifically optical coherence tomography angiography (OCTA) and adaptive optics (AO), in the diagnosis and monitoring of diabetic retinopathy. Methods: Our study represents an observational, cross-sectional analysis including sixty-nine patients from four distinct groups: a control group (17 patients), diabetic patients without diabetic retinopathy (no DR) (14 patients), diabetic patients with non-proliferative diabetic retinopathy (NPDR) (18 patients), and diabetic patients with proliferative diabetic retinopathy (PDR patients). A comprehensive ophthalmological evaluation, along with high-resolution imaging using OCTA and AO, was performed. OCTA images of the superficial capillary plexus, acquired with the OCT Angio Topcon, were analyzed using a custom-developed MATLAB algorithm, while AO retinal vascular images were evaluated with the manufacturer’s software of the Adaptive Optics Retinal Camera rtx1™. Results: Our findings demonstrated statistically significant reductions in foveal avascular zone circularity, superficial capillary plexus density, vessel length density, and fractal dimension, correlating with the severity of diabetic retinopathy, particularly in the PDR. Additionally, mean wall thickness and wall-to-lumen ratio were significantly increased in patients with diabetic retinopathy, notably in PDR. Conclusions: In conclusion, our findings demonstrate that the combined use of OCTA and AO imaging offers complementary insights into the microvascular alterations associated with diabetic retinopathy progression and severity. These high-resolution modalities together reveal both perfusion deficits and structural vascular changes, underscoring their utility as essential tools for early detection, staging, monitoring, and informed management of DR. Full article

This paper presents FWStab, a specialized video stabilization dataset tailored for flapping-wing platforms. The dataset encompasses five typical flight scenarios, featuring 48 video clips with intense dynamic jitter. The corresponding Inertial Measurement Unit (IMU) sensor data are synchronously collected, which jointly provide reliable support for multimodal modeling. Based on this, to address the issue of poor image acquisition quality due to severe vibrations in aerial vehicles, this paper proposes a multi-modal signal fusion video stabilization framework. This framework effectively integrates image features and inertial sensor features to predict smooth and stable camera poses. During the video stabilization process, the true camera motion originally estimated based on sensors is warped to the smooth trajectory predicted by the network, thereby optimizing the inter-frame stability. This approach maintains the global rigidity of scene motion, avoids visual artifacts caused by traditional dense optical flow-based spatiotemporal warping, and rectifies rolling shutter-induced distortions. Furthermore, the network is trained in an unsupervised manner by leveraging a joint loss function that integrates camera pose smoothness and optical flow residuals. When coupled with a multi-stage training strategy, this framework demonstrates remarkable stabilization adaptability across a wide range of scenarios. The entire framework employs Long Short-Term Memory (LSTM) to model the temporal characteristics of camera trajectories, enabling high-precision prediction of smooth trajectories. Full article

Background: Age-related macular degeneration (AMD) is one of the leading causes of permanent vision loss in the elderly, particularly in higher-income countries. Fundus autofluorescence (FAF) imaging is a widely used, non-invasive technique that complements structural imaging in the assessment of retinal pigment epithelium (RPE) integrity. While optical coherence tomography (OCT) remains the gold standard for retinal imaging due to its high-resolution cross-sectional visualization, FAF offers unique metabolic insights. Among the FAF modalities, blue light FAF (B-FAF) is more commonly employed, whereas green light FAF (G-FAF) provides subtly different image characteristics, particularly improved visualization and contrast in the central macula. Despite identical acquisition times and nearly indistinguishable workflows, G-FAF is notably underutilized in clinical practice. Objectives: This narrative review critically compares green and blue FAF in terms of their diagnostic utility relative to OCT, with a focus on lesion detectability, macular pigment interference, and clinical decision-making in retinal disorders. Methods: A comprehensive literature search was performed using the PubMed database for studies published prior to February 2025. The search utilized the keywords fundus autofluorescence and age-related macular degeneration. The primary focus was on short-wavelength FAF and its clinical utility in AMD, considering three aspects: diagnosis, follow-up, and prognosis. The OCT findings served as the reference standard for anatomical correlation and diagnostic accuracy. Results: Both FAF modalities correlated well with OCT in detecting RPE abnormalities. G-FAF demonstrated improved visibility of central lesions due to reduced masking by macular pigment and enhanced contrast in the macula. However, clinical preference remained skewed toward B-FAF, driven more by tradition and device default settings than by evidence-based superiority. G-FAF’s diagnostic potential remains underrecognized despite its comparable practicality and subtle imaging advantages specifically for AMD patients. AMD stages were accurately characterized, and relevant images were used to highlight the significance of G-FAF and B-FAF in the examination of AMD patients. Conclusions: While OCT remains the gold standard, FAF provides complementary information that can guide management strategy. Since G-FAF is functionally equivalent in acquisition, it offers slight advantages. Broader awareness and more frequent integration of G-FAF that could optimize multimodal imaging strategies, particularly in the intermediate stage, should be developed. Full article

Background: Human induced pluripotent stem cell cardiomyocytes (hiPSC-CMs) allow for high-throughput evaluation of cardiomyocyte (CM) physiology in health and disease. While multimodality testing provides a large breadth of information related to electrophysiology, contractility, and intracellular signaling in small populations of iPSC-CMs, current technologies for analyzing these parameters are expensive and resource-intensive. Methods: We have designed a novel 2D/3D hybrid organoid system that can harness optical imaging techniques to assess electromechanical properties and calcium dynamics across CMs in a high-throughput manner. We validated our methods using a doxorubicin-based system, as the drug has well-characterized cardiotoxic, pro-arrhythmic effects. Results: This novel hybrid system provides the functional benefit of 3D organoids while minimizing optical interference from multilayered cellular systems through our cell-culture techniques that propagate organoids outwards into 2D iPSC-CM sheets. The organoids recapitulate contractile forces that are more robust in 3D structures and connectivity, while 2D CMs facilitate analysis at an individual cellular level, which recreated numerous doxorubicin-induced electrophysiologic and propagation abnormalities. Conclusions: Thus, we have developed a novel 2D/3D hybrid organoid model that employs an integrated optical analysis platform to provide a reliable high-throughput method for studying cardiotoxicity, providing valuable data on calcium, contractility, and signal propagation. Full article

Remote sensing tasks, such as land cover classification, are increasingly becoming multimodal problems, where information from multiple imaging devices, complementing each other, can be fused. In particular, synergies between optical and synthetic aperture radar (SAR) are widely recognized to be beneficial in a variety of tasks. At the same time, archival of multimodal imagery for global coverage poses significant storage requirements due to the multitude of available sensors, and their increasingly higher resolutions. In this paper, we exploit redundancies between SAR and optical imaging modalities to create a joint encoding that improves storage efficiency. A novel neural network design with progressive attentive fusion modules is proposed for joint compression. The model is also promptable at test time with a desired tradeoff between the input modalities, to enable flexibility in the fidelity of the joint representation to each of them. Moreover, we show how end-to-end optimization of the joint compression model, including its modality tradeoff prompt, allows for better accuracy on downstream tasks leveraging multimodal inference when a constraint on the rate is to be met. Full article

The spread of fake news on social media is complicated by the fact that fake information spreads extremely fast in both textual and visual formats. Traditional approaches to the detection of fake news focus mainly on text and image features, thereby missing valuable information contained within images and texts. In response to this, we propose a multimodal fake news detection method based on BERT, with an extension to text combined with the extracted text from images through Optical Character Recognition (OCR). Here, we consider extending feature analysis with BERT_base_uncased to process inputs for retrieving relevant text from images and determining a confidence score that suggests the probability of the news being authentic. We report extensive experimental results on the ISOT, WELFAKE, TRUTHSEEKER, and ISOT_WELFAKE_TRUTHSEEKER datasets. Our proposed model demonstrates better generalization on the TRUTHSEEKER dataset with an accuracy of 99.97%, achieving substantial improvements over existing methods with an F1-score of 0.98. Experimental results indicate a potential accuracy increment of +3.35% compared to the latest baselines. These results highlight the potential of our approach to serve as a strong resource for automatic fake news detection by effectively integrating both text and visual data streams. Findings suggest that using diverse datasets enhances the resilience of detection systems against misinformation strategies. Full article

Optical Coherence Tomography (OCT) is a relatively new medical imaging device that provides high-resolution and real-time visualization of biological tissues. Initially designed for ophthalmology, OCT is now being applied in other types of pathologies, like cancer diagnosis. This review highlights its impact on disease diagnosis, biopsy guidance, and treatment monitoring. Despite its advantages, OCT has limitations, particularly in tissue penetration and differentiating between malignant and benign lesions. To overcome these challenges, the integration of nanoparticles has emerged as a transformative approach, which significantly enhances contrast and tumor vascularization at the molecular level. Gold and superparamagnetic iron oxide nanoparticles, for instance, have demonstrated great potential in increasing OCT’s diagnostic accuracy through enhanced optical scattering and targeted biomarker detection. Beyond these innovations, integrating OCT with multimodal imaging methods, including magnetic resonance imaging (MRI), positron emission tomography (PET), and ultrasound, offers a more comprehensive approach to disease assessment, particularly in oncology. Additionally, advances in artificial intelligence (AI) and biosensors have further expanded OCT’s capabilities, enabling real-time tumor characterization and optimizing surgical precision. However, despite these advancements, clinical adoption still faces several hurdles. Issues related to nanoparticle biocompatibility, regulatory approvals, and standardization need to be addressed. Moving forward, research should focus on refining nanoparticle technology, improving AI-driven image analysis, and ensuring broader accessibility to OCT-guided diagnostics. By tackling these challenges, OCT could become an essential tool in precision medicine, facilitating early disease detection, real-time monitoring, and personalized treatment for improved patient outcomes. Full article

Oral squamous cell carcinoma (OSCC), a major subtype of head and neck squamous cell carcinoma (HNSCC), is a significant global health burden owing to its late-stage diagnosis and poor prognosis. Recent advancements in molecular biology, genomics, and imaging have transformed the landscape of OSCC diagnosis and treatment. This review provides a comprehensive synthesis of early molecular diagnostic strategies, including biomarker discovery using next-generation sequencing, liquid biopsy, and salivary exosomal microRNAs. In addition, we highlight the emerging role of non-invasive optical imaging technologies and their clinical integration for improved surgical precision and early lesion detection. This review also discusses evolving therapeutic approaches, including immunotherapy, neoadjuvant chemotherapy, and patient-centered multimodal regimens tailored through molecular profiling. We emphasized balancing therapeutic efficacy with the quality of life in patients undergoing chemoradiotherapy. The convergence of multi-omics, artificial intelligence, and precision medicine holds promise for revolutionizing early detection and personalized treatment of OSCC, ultimately improving patient survival and clinical outcomes. Full article

Deep neural network fusion approaches utilizing multimodal remote sensing are essential for crop mapping. However, challenges such as insufficient spatiotemporal feature extraction and ineffective fusion strategies still exist, leading to a decrease in mapping accuracy and robustness when these approaches are applied across spatial‒temporal regions. In this study, we propose a novel rice mapping approach based on dual-branch transformer fusion networks, named RDTFNet. Specifically, we implemented a dual-branch encoder that is based on two improved transformer architectures. One is a multiscale transformer block used to extract spatial–spectral features from a single-phase optical image, and the other is a Restormer block used to extract spatial–temporal features from time-series synthetic aperture radar (SAR) images. Both extracted features were then combined into a feature fusion module (FFM) to generate fully fused spatial–temporal–spectral (STS) features, which were finally fed into the decoder of the U-Net structure for rice mapping. The model’s performance was evaluated through experiments with the Sentinel-1 and Sentinel-2 datasets from the United States. Compared with conventional models, the RDTFNet model achieved the best performance, and the overall accuracy (OA), intersection over union (IoU), precision, recall and F1-score were 96.95%, 88.12%, 95.14%, 92.27% and 93.68%, respectively. The comparative results show that the OA, IoU, accuracy, recall and F1-score improved by 1.61%, 5.37%, 5.16%, 1.12% and 2.53%, respectively, over those of the baseline model, demonstrating its superior performance for rice mapping. Furthermore, in subsequent cross-regional and cross-temporal tests, RDTFNet outperformed other classical models, achieving improvements of 7.11% and 12.10% in F1-score, and 11.55% and 18.18% in IoU, respectively. These results further confirm the robustness of the proposed model. Therefore, the proposed RDTFNet model can effectively fuse STS features from multimodal images and exhibit strong generalization capabilities, providing valuable information for governments in agricultural management. Full article

Progression independent of relapse activity (PIRA) is increasingly recognized as a key driver of disability in multiple sclerosis (MS). However, the concept of PIRA remains elusive, with uncertainty surrounding its definition, underlying mechanisms, and methods of quantification. This review examines the current landscape of biomarkers used to predict and measure PIRA, focusing on clinical, imaging, and body fluid biomarkers. Clinical disability scores such as the Expanded Disability Status Scale (EDSS) are widely used, but may lack sensitivity in capturing subtle relapse-independent progression. Imaging biomarkers, including MRI-derived metrics (brain and spinal cord volume loss, chronic active lesions) and optical coherence tomography (OCT) parameters (retinal nerve fiber layer and ganglion cell-inner plexiform layer thinning), offer valuable insights, but often reflect both inflammatory and neurodegenerative processes. Body fluid biomarkers, such as neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP), are promising indicators of axonal damage and glial activation, but their specificity for PIRA remains limited. This review emphasizes the distinction between predicting PIRA—identifying individuals at risk of future progression—and measuring ongoing PIRA-related disability in real time. We highlight the limitations of current biomarkers in differentiating PIRA from relapse-associated activity and call for a clearer conceptual framework to guide future research. Advancing the precision and utility of PIRA biomarkers will require multimodal approaches, longitudinal studies, and standardized protocols to enable their clinical integration and to improve personalized MS management. Full article