Search Results (347)

Oesophageal cancer remains a significant global health burden with poor survival outcomes despite multimodal treatment. Recent advances in magnetic resonance imaging (MRI) have opened opportunities to improve radiotherapy delivery. This review examines the role of MRI and MR-guided radiotherapy (MRgRT) in oesophageal cancer, focusing on applications in staging, treatment planning, and response assessment, with particular emphasis on magnetic resonance linear accelerator (MR-Linac)-based delivery. Compared to computed tomography (CT), MRI offers superior soft-tissue contrast, enabling more accurate tumour delineation and the potential for reduced treatment margins. Real-time MR imaging during treatment can facilitate motion management, while daily adaptive planning can accommodate anatomical changes throughout the treatment course. Functional MRI sequences, including diffusion-weighted and dynamic contrast-enhanced imaging, offer quantitative data for treatment response monitoring. Early clinical and dosimetric studies demonstrate that MRgRT can significantly reduce radiation dose to critical organs while maintaining target coverage. However, clinical evidence for MRgRT in oesophageal cancer is limited to small early-phase studies, with no phase II/III trials demonstrating improvements in survival, toxicity, or patient-reported outcomes. Long-term clinical benefits and cost-effectiveness remain unproven, highlighting the need for prospective outcome-focused studies to define the role for MRgRT within multimodality treatment pathways. Full article

Artificial intelligence (AI) is increasingly reshaping the control mechanisms that govern magnetic resonance imaging (MRI), enabling faster, safer, and more adaptive operation of the scanner’s physical subsystems. This review provides a comprehensive survey of recent AI-driven advances in core control domains: radio frequency (RF) pulse design and specific absorption rate (SAR) prediction, motion-dependent modeling of $B_1^{+}$ and $B_0$ fields, and gradient system characterization and correction. Across these domains, deep learning models—convolutional, recurrent, generative, and temporal convolutional networks—have emerged as powerful computational surrogates for numerical electromagnetic simulations, Bloch simulations, motion tracking, and gradient impulse response modeling. These networks achieve subject-specific field or SAR predictions within seconds or milliseconds, mitigating long-standing limitations associated with inter-subject variability, non-linear system behavior, and the need for extensive calibration. We highlight methodological themes such as physics-guided training, reinforcement learning for RF pulse design, subject-specific fine-tuning, uncertainty considerations, and the integration of learned models into real-time MRI workflows. Open challenges and future directions include unified multi-physics frameworks, deep learning approaches for generalizing across anatomies and coil configurations, robust validation across vendors and field strengths, and safety-aware AI design. Overall, AI-powered control strategies are poised to become foundational components of next-generation, high-performance, and personalized MRI systems. Full article

Multiple sclerosis (MS) is a heterogeneous autoimmune disease driven by peripheral immune dysregulation and compartmentalized central nervous system (CNS) inflammation. Despite more than 20 approved disease-modifying therapies, disability accrual remains common, particularly in patients with highly active relapsing disease and progressive phenotypes characterized by silent progression and smoldering neuroinflammation. Two emerging therapeutic strategies address these unmet needs: Bruton’s tyrosine kinase (BTK) inhibitors and autologous haematopoietic stem cell transplantation (HSCT). Although mechanistically distinct, both aim to overcome limitations of conventional immunosuppression by intervening more deeply in the autoimmune cascade. This narrative review synthesized mechanistic, clinical, and translational evidence identified through a comprehensive search of PubMed, Scopus, Web of Science, and ClinicalTrials.gov from January 2010 to August 2025. BTK inhibitors are oral, CNS-penetrant therapies that selectively modulate B-cell signaling and CNS-resident myeloid cells without broad lymphocyte depletion, enabling continuous immunomodulation. Phase II–III trials of evobrutinib, tolebrutinib, and fenebrutinib show consistent MRI activity suppression but variable effects on relapses and disability, suggesting relevance in microglial-driven, relapse-independent disease. HSCT is a one-time immune reconstitution therapy that eradicates autoreactive immune clones and restores immune tolerance. Randomized and real-world studies demonstrate profound suppression of inflammatory activity, stabilization or improvement of disability, and durable treatment-free remission in selected patients with highly active relapsing–remitting MS, although procedure-related risks require strict eligibility criteria and experienced centers. Together with BTK inhibitors, HSCT represents a complementary strategy within an increasingly personalized MS treatment paradigm, emphasizing biomarker-guided patient selection and optimized therapeutic sequencing. Full article

Background/Objectives: This systematic review examines neuroplasticity-informed approaches to learning under cognitive load, synthesizing evidence from functional imaging, brain stimulation, and educational technology research. As digital learning environments increasingly challenge learners with complex cognitive demands, understanding how neuroplasticity principles can inform adaptive educational design becomes critical. This review examines how neural mechanisms underlying learning under cognitive load can inform the development of evidence-based educational technologies that optimize neuroplastic potential while mitigating cognitive overload. Methods: Following PRISMA guidelines, we synthesized 94 empirical studies published between 2005 and 2025 across PubMed, Scopus, Web of Science, and PsycINFO. Studies were selected based on rigorous inclusion criteria that emphasized functional neuroimaging (fMRI, EEG), non-invasive brain stimulation (tDCS, TMS), and educational technology applications, which examined learning outcomes under varying cognitive load conditions. Priority was given to research with translational implications for adaptive learning systems and personalized educational interventions. Results: Functional imaging studies reveal an inverted-U relationship between cognitive load and neuroplasticity, with a moderate challenge in optimizing prefrontal-parietal network activation and learning-related neural adaptations. Brain stimulation research demonstrates that tDCS and TMS can enhance neuroplastic responses under cognitive load, particularly benefiting learners with lower baseline abilities. Educational technology applications demonstrate that neuroplasticity-informed adaptive systems, which incorporate real-time cognitive load monitoring and dynamic difficulty adjustment, significantly enhance learning outcomes compared to traditional approaches. Individual differences in cognitive capacity, neurodiversity, and baseline brain states substantially moderate these effects, necessitating the development of personalized intervention strategies. Conclusions: Neuroplasticity-informed learning approaches offer a robust framework for educational technology design that respects cognitive load limitations while maximizing adaptive neural changes. Integration of functional imaging insights, brain stimulation protocols, and adaptive algorithms enables the development of inclusive educational technologies that support diverse learners under cognitive stress. Future research should focus on scalable implementations of real-time neuroplasticity monitoring in authentic educational settings, as well as on developing ethical frameworks for deploying neurotechnology-enhanced learning systems across diverse populations. Full article

Microrobots are emerging as transformative tools in minimally invasive medicine, with applications in non-invasive therapy, real-time diagnosis, and targeted drug delivery. Effective use of these systems critically depends on accurate detection and tracking of microrobots within the body. Among commonly used imaging modalities, including MRI, CT, and optical imaging, ultrasound (US) offers an advantageous balance of portability, low cost, non-ionizing safety, and high temporal resolution, making it particularly suitable for real-time microrobot monitoring. This study reviews current detection strategies and presents a comparative evaluation of six advanced AI-based multi-object detectors, including ConvNeXt, Res2NeXt-101, ResNeSt-269, U-Net, and the latest YOLO variants (v11, v12), being applied to microrobot detection in US imaging. Performance is assessed using standard metrics (AP50–95, precision, recall, F1-score) and robustness to four visual perturbations: blur, brightness variation, occlusion, and speckle noise. Additionally, feature-level sensitivity analyses are conducted to identify the contributions of different visual cues. Computational efficiency is also measured to assess suitability for real-time deployment. Results show that ResNeSt-269 achieved the highest detection accuracy, followed by Res2NeXt-101 and ConvNeXt, while YOLO-based detectors provided superior computational efficiency. These findings offer actionable insights for developing robust and efficient microrobot tracking systems with strong potential in diagnostic and therapeutic healthcare applications. Full article

Ovarian cancer continues to be the most lethal gynaecological malignancy, principally due to its late-stage diagnosis, extensive peritoneal dissemination, chemoresistance, and limitations of current imaging and therapeutic strategies. By optimising pharmacokinetics, refining tumour-selective drug delivery, and supporting high-resolution, multimodal imaging, nanomedicine offers a versatile platform to address these limitations. In this review, current progress across lipid-based, polymeric, inorganic, hybrid, and biomimetic nanocarriers is synthesised, emphasising how tailored physiochemical properties, surface functionalisation, and stimuli-responsive designs can improve tumour localisation, surmount stromal and ascetic barriers, and enable controlled drug release. Concurrently, significant advancement in imaging nanoprobes, including magnetic resonance imaging (MRI), positron emission tomography (PET)/single-photon emission computed tomography (SPECT), optical, near-infrared imaging (NIR), ultrasound, and photoacoustic systems, has evolved early lesion detection, intraoperative guidance, and quantitative monitoring of treatment. Diagnosis and therapy are further integrated within single platforms by emerging theranostic constructs, encouraging real-time visualisation of drug distribution and treatment response. Additionally, immune-nanomedicine, intraperitoneal depot systems, and nucleic acid-centred nanotherapies offer promising strategies to address immune suppression and molecular resistance in advanced ovarian cancer. In spite of noteworthy achievements, clinical translation is limited by complex manufacturing requirements, challenges with safety and stability, and restricted patient stratification. To unlock the full clinical potential of nanotechnology in ovarian cancer management, constant innovation in scalable design, regulatory standardisation, and integration of precision biomarkers will be necessary. Full article

Drug discovery is a complex and multi-stage process that requires advanced analytical technologies capable of accelerating preclinical evaluation and improving the precision of therapeutic design. The combination of fluorescence and magnetic resonance imaging (MRI) within multimodal imaging plays an increasingly important role in modern pharmacokinetics, integrating the high molecular sensitivity of fluorescence with the non-invasive anatomical visualization offered by MRI. Fluorescence enables real-time monitoring of cellular processes, including drug–target interactions and molecular dynamics, whereas MRI provides detailed structural information on tissues without exposure to ionizing radiation. Hybrid probes—such as superparamagnetic iron oxide nanoparticles (SPIONs) functionalized with near-infrared (NIR) fluorophores or gadolinium-based complexes linked to optical dyes—enable simultaneous acquisition of molecular and anatomical data in a single examination. These multimodal systems are being explored in oncology, neurology, and cardiology, where they support improved visualization of tumor biology, amyloid pathology, and inflammatory processes in vascular disease. Although multimodal imaging shows great promise for enhancing pharmacokinetic and pharmacodynamic studies, several challenges remain, including the potential toxicity of heavy-metal-based contrast agents, limited tissue penetration of fluorescence signals, probe stability in vivo, and the complexity and cost of synthesis. Advances in nanotechnology, particularly biodegradable carriers and manganese-based MRI contrasts, together with the integration of artificial intelligence algorithms, are helping to address these limitations. In the future, fluorescence–MRI hybrid imaging may become an important tool in personalized medicine, supporting more precise therapy planning and reducing the likelihood of clinical failure. Full article

Active propelled micro robots (MRs) represent a transformative shift in biomedical engineering, engineered to navigate physiological environments by converting chemical, acoustic, or magnetic energy into mechanical propulsion. Unlike passive delivery systems limited by diffusion and systemic clearance, MRs offer autonomous mobility, enabling precise penetration and retention in hard-to-reach tissues. This review provides comprehensive analysis of MR technologies within urology, a field uniquely suited for microrobotic intervention due to the urinary tract’s anatomical accessibility and fluid-filled nature. We explore how MRs address critical therapeutic limitations, including the high recurrence of kidney stones and the rapid washout of intravesical bladder cancer therapies. The review categorizes propulsion mechanisms optimized for the urinary environment, such as urea-fueled nanomotors and magnetic swarms. Furthermore, we detail emerging applications, including bioresorbable acoustic robots for tumor ablation and magnetic grippers for minimally invasive biopsies. Finally, we critically assess the path toward clinical translation, focusing on challenges in biocompatibility, real-time tracking (MRI, MPI, photoacoustic imaging), and the regulatory landscape for these advanced combination products. Full article

Breast cancer screening, while vital for reducing mortality, faces significant limitations in sensitivity and specificity, particularly in dense breasts. Current modalities primarily detect anatomical changes, often missing biologically aggressive tumors at their earliest stages. The altered metabolism of cancer cells establishes a characteristic inverted pH gradient that drives tumor invasion, metastasis, and treatment resistance. This makes tumor acidity a compelling, functional biomarker for early detection. This review synthesizes the emerging role of pH as a diagnostic biomarker and provides a critical evaluation of advanced imaging techniques for its non-invasive or minimal measurement. We detail the biological underpinnings of tumor acidosis, emphasizing its regulation through glycolytic reprogramming and dysregulated proton transport. Our analysis encompasses a broad spectrum of pH-sensitive imaging modalities, including magnetic resonance methods such as Chemical Exchange Saturation Transfer (CEST) MRI for extracellular pH mapping and multi-nuclear Magnetic Resonance Spectroscopy (MRS) using ¹H, ³¹P, and ¹⁹F nuclei to probe various cellular compartments. Furthermore, we examine hyperpolarized ¹³C MRI for real-time metabolic flux imaging, where metrics such as the lactate-to-pyruvate ratio demonstrate significant predictive value for treatment response. The review also assesses optical and photoacoustic imaging techniques, which offer high sensitivity but are often constrained to superficial tumors. Imaging tumor pH provides a powerful functional window into the earliest metabolic shifts in breast cancer, far preceding macroscopic anatomical changes. The ongoing development and evidence support the role of the pH-sensitive imaging techniques in diagnosis, lesion characterization, and therapy. Additionally, it holds promise for supplementing breast cancer screening by enabling earlier, more specific detection and personalized risk stratification, ultimately aiming to improve patient outcomes. Full article

With the increasing prevalence of brain tumors, it becomes crucial to ensure fast and reliable segmentation in MRI scans. Medical professionals struggle with manual tumor segmentation due to its exhausting and time-consuming nature. Automated segmentation speeds up decision-making and diagnosis; however, achieving an optimal balance between accuracy and computational cost remains a significant challenge. In many cases, current methods trade speed for accuracy, or vice versa, consuming substantial computing power and making them difficult to use on devices with limited resources. To address this issue, we present PRA-UNet, a lightweight deep learning model optimized for fast and accurate 2D brain tumor segmentation. Using a single 2D input, the architecture processes four types of MRI scans (FLAIR, T1, T1c, and T2). The encoder uses inverted residual blocks and bottleneck residual blocks to capture features at different scales effectively. The Convolutional Block Attention Module (CBAM) and the Spatial Attention Module (SAM) improve the bridge and skip connections by refining feature maps and making it easier to detect and localize brain tumors. The decoder uses depthwise separable convolutions, which significantly reduce computational costs without degrading accuracy. The BraTS2020 dataset shows that PRA-UNet achieves a Dice score of 95.71%, an accuracy of 99.61%, and a processing speed of 60 ms per image, enabling real-time analysis. PRA-UNet outperforms other models in segmentation while requiring less computing power, suggesting it could be suitable for deployment on lightweight edge devices in clinical settings. Its speed and reliability enable radiologists to diagnose tumors quickly and accurately, enhancing practical medical applications. Full article

Despite advances in deep learning, brain tumor detection from MRI continues to face major challenges, including the limited robustness of single-modality models, the computational burden of transformer-based architectures, opaque fusion strategies, and the lack of efficient binary screening tools. To address these issues, we propose a lightweight multimodal CNN framework that integrates T1, T2, and FLAIR MRI sequences using modality-specific encoders and a channel-wise fusion module (concatenation followed by a 1 × 1 convolution). The pipeline incorporates U-Net-based segmentation for tumor-focused patch extraction, improving localization and reducing irrelevant background. Evaluated on the BraTS 2020 dataset (7500 slices; 70/15/15 patient-level split), the proposed model achieves 93.8% accuracy, 94.1% F1-score, and 19 ms inference time. It outperforms all single-modality ablations by up to 5% and achieves competitive or superior performance to transformer-based baselines while using over 98% fewer parameters. Grad-CAM and LIME visualizations further confirm clinically meaningful tumor-region activation. Overall, this efficient and interpretable multimodal framework advances scalable brain tumor screening and supports integration into real-time clinical workflows. Full article

This paper introduces a novel framework for sensory representation of brain imaging data, combining deep learning-based segmentation with multimodal visual and auditory outputs. Structural magnetic resonance imaging (MRI) predictions are converted into color-coded maps and stereophonic/MIDI sonifications, enabling intuitive interpretation of cortical activation patterns. High-precision U-Net models efficiently generate these outputs, supporting clinical decision-making, cognitive research, and creative applications. Spatial, intensity, and anomalous features are encoded into perceivable visual and auditory cues, facilitating early detection and introducing the concept of “auditory biomarkers” for potential pathological identification. Despite current limitations, including dataset size, absence of clinical validation, and heuristic-based sonification, the pipeline demonstrates technical feasibility and robustness. Future work will focus on clinical user studies, the application of functional MRI (fMRI) time-series for dynamic sonification, and the integration of real-time emotional feedback in cinematic contexts. This multisensory approach offers a promising avenue for enhancing the interpretability of complex neuroimaging data across medical, research, and artistic domains. Full article

Breast cancer is one of the most prevalent malignant tumors worldwide, with the highest incidence and mortality among women. Early precise diagnosis and the development of efficient treatment regimens remain major clinical challenges. Harnessing the programmable size, surface chemistry, and tumor microenvironment (TME) responsiveness of nanomaterials, there is tremendous potential for their applications in breast cancer diagnosis and therapy. In the diagnostic arena, nanomaterials serve as core components of novel contrast agents (e.g., gold nanorods, quantum dots, superparamagnetic iron oxide nanoparticles) and biosensing platforms, substantially enhancing the sensitivity and specificity of molecular imaging modalities—such as magnetic resonance imaging (MRI), computed tomography (CT), and fluorescence imaging (FLI)—and enabling high-sensitivity detection of circulating tumor cells and tumor-derived exosomes, among various liquid biopsy biomarkers. In therapy, nanoscale carriers (e.g., liposomes, polymeric micelles) improve tumor targeting and accumulation efficiency through passive and active targeting strategies, thereby augmenting anticancer efficacy while effectively reducing systemic toxicity. Furthermore, nanotechnology has spurred the rapid advancement of emerging modalities, including photothermal therapy (PTT), photodynamic therapy (PDT), and immunotherapy. Notably, the construction of theranostic platforms that integrate diagnostic and therapeutic units within a single nanosystem enables in vivo, real-time visualization of drug delivery, treatment monitoring, and therapeutic response feedback, providing a powerful toolkit for advancing breast cancer toward personalized, precision medicine. Despite challenges that remain before clinical translation—such as biocompatibility, scalable manufacturing, and standardized evaluation—nanomaterials are undoubtedly reshaping the paradigm of breast cancer diagnosis and treatment. Full article

Background/Objectives: Hypoxic–ischemic encephalopathy (HIE) is a common neurological outcome of perinatal asphyxia, with cerebral palsy (CP) being the most severe lasting effect. Perinatal brain injury activates the immune system and induces the release of inflammatory mediators. Matrix Metalloproteinases (MMPs) play a crucial role in neuroinflammation and neurodegeneration. This study explored the potential link between MMP2 promoter polymorphisms and the development of CP in children with a history of perinatal asphyxia. Methods: We enrolled 212 patients (130 males and 82 females) with documented perinatal asphyxia, who underwent a comprehensive neurological assessment and neuroimaging, including ultrasound and magnetic resonance imaging (MRI). We genotyped the MMP2 promoter polymorphisms rs243866, rs243865, and rs243864 using real-time polymerase chain reaction. Haplotype frequencies were calculated using Haploview software. Results: As expected, patients with HIE are more likely to develop CP (p = 0.000). In a study of 104 patients who developed CP, the frequencies of the A (rs243866), T (rs243865), and G alleles (rs243864) were nearly twice as high compared to those without CP (p = 0.008, p = 0.019, and p = 0.008, respectively). Haplotype analysis supported these findings, showing that the ATG haplotype was significantly more common among patients who developed CP (p = 0.004). Additionally, in patients with MRI-confirmed brain damage, the ATG haplotype was more frequently observed (p = 0.019). Conclusions: The ATG haplotype of the MMP2 promoter may indicate a risk factor for developing cerebral palsy (CP) in patients who experience perinatal asphyxia and could serve as a potential diagnostic predictor of CP. Full article

Background: Practical, quantitative ultrasound-based tools for measuring hepatic steatosis are needed in everyday MASLD care. We evaluated a new multiparametric quantitative ultrasound (QUS) platform that integrates ultrasound-guided fat fraction (UGFF), attenuation coefficient (AC), backscatter coefficient (BSC), and signal-to-noise ratio (SNR), using Controlled Attenuation Parameter (CAP) as the reference and examining the effect of breathing. Methods: In a prospective single-center study, adult patients underwent same-day liver QUS and FibroScan. QUS measurements were performed during breath-hold and during normal breathing. Regions of interest were placed in right-lobe parenchyma 2 cm below the capsule, avoiding vessels. Primary outcomes were correlation with CAP and ROC performance at CAP cutoffs for S1 (≥230 dB/m), S2 (≥275 dB/m), and S3 (≥300 dB/m). Results: QUS was feasible in almost all examinations. UGFF, BSC, and SNR were consistent across breathing conditions, while AC was slightly higher during normal breathing. UGFF showed strong correlation with CAP and high accuracy for detecting steatosis. Across grades, AUCs were around 0.89–0.91, with cutoffs (UGFF ≈ 4% for ≥S1 and ≈11% for ≥S3). Conclusions: Multiparametric QUS provides reliable liver fat quantification that aligns closely with CAP and remains robust in practice whether patients hold their breath or breathe normally. These findings support UGFF as a practical, reliable point-of-care alternative for liver fat quantification that can be embedded in routine ultrasound in real time. Validation against MRI-PDFF or histology and multicenter studies will further define cutoffs and generalizability. Full article