Search Results (2,583)

Dynamic contrast-enhancement (DCE) modality of MRI is typically considered secondary in prostate cancer (PCa) diagnostics, due to the common interpretation that its diagnostic power is lower than that of other modalities like T2-weighted (T2W) or diffusion-weighted imaging (DWI). To challenge this paradigm, this study introduces a novel concept of a difference map, which relies exclusively on DCE-MRI for the localization of peripheral zone prostate cancer using functional data analysis-based (FDA) signal processing. The proposed workflow uses discrete voxel-level DCE time–signal curves that are transformed into a continuous functional form. First-order derivatives are then used to determine patient-specific time points of greatest enhancement change that adapt to the intrinsic characteristics of each patient, producing diffmaps that highlight regions with pronounced enhancement dynamics, indicative of malignancy. A subsequent normalization step accounts for inter-patient variability, enabling consistent interpretation across subjects and probabilistic PCa localization. The approach is validated on a curated dataset of 20 patients. Evaluation of eight workflow variants is performed using weighted log loss, the best variant achieving a mean log loss of 0.578. This study demonstrates the feasibility and effectiveness of a single-modality, automated, and interpretable approach for peripheral prostate cancer localization based solely on DCE-MRI. Full article

(1) Purpose: Thoracic ossification of the ligamentum flavum (OLF) is increasingly recognized in East Asian populations, but reliable estimates in clinical settings remain limited. This study aimed to determine the clinic-based, lower thoracic (T8–T12) MRI prevalence of OLF among patients undergoing lumbar spine MRI for low-back pain and to identify radiological features associated with OLF. (2) Materials and Method: A cohort of patients with lower back pain who underwent L-Spine MRI studies in a tertiary medical center from January 2008 to December 2009 was created. Patients with thoracic OLF were identified, and a twice-fold sex-and-age-matched control group of patients without OLF, was randomly extracted. Radiological features in two groups were compared. (3) Results: The lower thoracic prevalence of OLF was 2.7%, significantly increasing in patients aged ≥60 years. OLF was most frequently involved in level T10-T11 (43%), and 23 cases (36%) showed multiple-level involvement. OLF was strongly associated with localized degenerative changes at the affected level, including higher degree of degenerative disc change, disc height loss, and more osteophyte formations. (4) Conclusions: Thoracic OLF is not a rare condition in patients with lower back pain. Patients with thoracic OLF were more likely to show features of focal degenerative changes, such as disc degeneration, osteophyte formation, and disc height loss on the level of OLF. Therefore, if initial plain radiographs of patients with neurologic deficits show evidence of degenerative change in the lower thoracic spine, a higher index of suspicion for thoracic OLF should prompt further evaluation. Full article

Background/Objectives: Diagnosing Rotator Cuff Tears (RCTs) via Magnetic Resonance Imaging (MRI) is clinically challenging due to complex 3D anatomy and significant interobserver variability. Traditional slice-centric Convolutional Neural Networks (CNNs) often fail to capture the necessary volumetric context for accurate grading. This study aims to develop and validate the Patient-Aware Vision Transformer (Pa-ViT), an explainable deep-learning framework designed for the automated, patient-level classification of RCTs (Normal, Partial-Thickness, and Full-Thickness). Methods: A large-scale retrospective dataset comprising 2447 T2-weighted coronal shoulder MRI examinations was utilized. The proposed Pa-ViT framework employs a Vision Transformer (ViT-Base) backbone within a Weakly-Supervised Multiple Instance Learning (MIL) paradigm to aggregate slice-level semantic features into a unified patient diagnosis. The model was trained using a weighted cross-entropy loss to address class imbalance and was benchmarked against widely used CNN architectures and traditional machine-learning classifiers. Results: The Pa-ViT model achieved a high overall accuracy of 91% and a macro-averaged F1-score of 0.91, significantly outperforming the standard VGG-16 baseline (87%). Notably, the model demonstrated superior discriminative power for the challenging Partial-Thickness Tear class (ROC AUC: 0.903). Furthermore, Attention Rollout visualizations confirmed the model’s reliance on genuine anatomical features, such as the supraspinatus footprint, rather than artifacts. Conclusions: By effectively modeling long-range dependencies, the Pa-ViT framework provides a robust alternative to traditional CNNs. It offers a clinically viable, explainable decision support tool that enhances diagnostic sensitivity, particularly for subtle partial-thickness tears. Full article

Gadolinium-doped hydroxyapatite nanoparticles (HapGd NPs) have emerged as promising multifunctional platforms for biomedical applications due to their unique combination of biocompatibility, structural tunability, and magnetic responsiveness. In this work, HapGd nanoparticles were synthesized using a microwave-assisted method and subsequently functionalized with curcumin and folic acid to enhance therapeutic efficiency and selective targeting. The synthesized nanostructures were characterized using various techniques, including X-ray diffraction (XRD), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), vibrating sample magnetometry (VSM), and relaxometry. Structural analyses revealed successful incorporation of Gd³⁺ ions into the Hap lattice, resulting in reduced unit cell volume and slight lattice distortion, while preserving the apatite crystalline framework. Surface functionalization with curcumin and folic acid was confirmed through spectroscopic characterization, demonstrating effective molecular attachment. Nuclear Magnetic Resonance (NMR) relaxation measurements indicated that Gd doping endowed paramagnetic behavior suitable for contrast enhancement in magnetic resonance imaging (MRI). Relaxometry studies revealed a strong linear correlation between 1/T₁ and the Gd³⁺ concentration, especially in the functionalized samples, with performance comparable to the commercial contrast agent Omniscan™. The developed HapGd-based nanoplatform exhibits integrated diagnostic and therapeutic potential, providing a foundation for future research in biomedical applications. Full article

Background/Objectives: This study evaluated the clinical image quality of three-dimensional synthetic MRI (3D SI) compared with conventional MRI (cMRI), focusing on tissue contrast, anatomical detail, and motion sensitivity. Methods: Patients with nonspecific neurological symptoms were included. Both cMRI and 3D SI were acquired on single-vendor 1.5 T and 3 T scanners with slice thicknesses of 1.0–1.7 mm. Two experienced neuroradiologists and one fellow independently evaluated matched scans using a 0–100 scale. Assessed parameters included signal-to-noise ratio (SNR), gray–white matter contrast, artifacts, motion robustness, and confidence in detecting perivascular spaces, white matter lesions, and subtle pathology. Interrater agreement was measured using Krippendorff’s alpha and ICC2. Multiple linear regression analyzed associations between image quality ratings and imaging method. Results: Images of 31 patients were analyzed. Three-dimensional SI demonstrated sufficient-to-good overall image quality and high robustness to motion. Cortical-surface-to-cerebrospinal-fluid contrast on FLAIR was rated lower for 3D SI than for cMRI. False-positive lesion detection occurred more frequently on 3D SI FLAIR, particularly among experienced readers. cMRI achieved significantly higher T1-weighted SNR than 3D SI (8.76 points, p < 0.001). Experienced readers consistently rated SNR and tissue contrast higher than the fellow. Vascular signal range was broader on 3D SI, reducing sensitivity to vascular abnormalities. Conclusions: Three-dimensional synthetic MRI provides clinically usable image quality and fulfills its primary diagnostic purpose, offering advantages in acquisition efficiency and robustness to motion. Nevertheless, limitations in cortical contrast, vascular signal characterization, and reader-dependent interpretive variability constrain its reliability for subtle or detail-critical findings. Full article

Magnetic resonance imaging (MRI) super-resolution (SR) enables high-resolution reconstruction from low-resolution acquisitions, reducing scan time and easing hardware demands. However, most deep learning-based SR models are large and computationally heavy, limiting deployment in clinical workstations, real-time pipelines, and resource-restricted platforms such as low-field and portable MRI. We introduce CHARMS, a lightweight convolutional–Transformer hybrid with attention regularization optimized for MRI SR. CHARMS employs a Reverse Residual Attention Fusion backbone for hierarchical local feature extraction, Pixel–Channel and Enhanced Spatial Attention for fine-grained feature calibration, and a Multi-Depthwise Dilated Transformer Attention block for efficient long-range dependency modeling. Novel attention regularization suppresses redundant activations, stabilizes training, and enhances generalization across contrasts and field strengths. Across IXI, Human Connectome Project Young Adult, and paired 3T/7T datasets, CHARMS (~1.9M parameters; ~30 GFLOPs for 256 × 256) surpasses leading lightweight and hybrid baselines (EDSR, PAN, W2AMSN-S, and FMEN) by 0.1–0.6 dB PSNR and up to 1% SSIM at ×2/×4 upscaling, while reducing inference time ~40%. Cross-field fine-tuning yields 7T-like reconstructions from 3T inputs with ~6 dB PSNR and 0.12 SSIM gains over native 3T. With near-real-time performance (~11 ms/slice, ~1.6–1.9 s per 3D volume on RTX 4090), CHARMS offers a compelling fidelity–efficiency balance for clinical workflows, accelerated protocols, and portable MRI. Full article

Chronic pain is a pervasive and debilitating condition that affects millions of individuals worldwide. Unlike acute pain, which serves a protective physiological role, chronic pain persists beyond routine tissue healing and often arises without a discernible peripheral cause. Accumulating evidence indicates that chronic pain is not merely a symptom but a disorder of the central nervous system, underpinned by interacting molecular, neurochemical, and network-level alterations. Molecular neuroimaging using PET and MR spectroscopy has revealed dysregulated excitatory–inhibitory balance (glutamate/GABA), altered monoaminergic and opioidergic signaling, and neuroimmune activation (e.g., TSPO-indexed glial activation) in key pain-related regions such as the insula, anterior cingulate cortex, thalamus, and prefrontal cortex. Converging multimodal imaging—including functional MRI, diffusion MRI, and EEG/MEG—demonstrates aberrant activity and connectivity across the default mode, salience, and sensorimotor networks, alongside structural remodeling in cortical and subcortical circuits. Parallel advances in neuromodulation, including transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), deep brain stimulation (DBS), and emerging biomarker-guided closed-loop approaches, provide tools to perturb these maladaptive circuits and to test mechanistic hypotheses in vivo. This review integrates neuroimaging findings with molecular and systems-level mechanistic insights into chronic pain and its modulation, highlighting how imaging markers can link biochemical signatures to neural dynamics and guide precision pain management and individualized therapeutic strategies. Full article

Independent vector analysis (IVA) has emerged as a powerful tool for fusing and analyzing functional magnetic resonance imaging (fMRI) data. Applying IVA to multi-task fMRI data enhances analytical power by capturing the relationships across different tasks in order to discover their underlying multivariate relationship to one another. Incorporation of prior information into IVA enhances the separability and interpretability of estimated components. In this paper, we demonstrate successful fusion of multi-task fMRI feature data under two settings: constrained IVA and constrained transposed IVA (tIVA). We show that using these methods for fusing multi-task fMRI feature data offers novel ways to improve the quality and interpretability of the analysis. While constrained IVA extracts components linked to distinct brain networks, tIVA reverses the roles of spatial components and subject profiles, enabling flexible analysis of behavioral effects. We apply both methods to a multi-task fMRI dataset of 247 subjects. We demonstrate that for task-based fMRI, structural MRI (sMRI) references provide a better match for task data than resting-state fMRI (rs-fMRI) references, and using sMRI priors improves identification of group differences in task-related networks, such as the sensory-motor network during the Auditory Oddball (AOD) task. Additionally, constrained tIVA allows for targeted investigation of the effects of behavioral variables by applying them individually during the analysis. For instance, by using the letter number sequence subtest, a measure of working memory, as a behavioral constraint in tIVA, we observed significant group differences in the auditory and sensory-motor networks during the AOD task. Results show that the use of two constrained approaches, guided by well-aligned structural and behavioral references, enables a more comprehensive analysis of underlying brain function as modulated by task. Full article

Background/Objectives: Interest in myocardial mapping for cardiac MRI has increased, enabling differentiation of various cardiac diseases through T1, T2, and T2* mapping. This study evaluates the impact of deep learning (DL)-based image reconstruction on the mean and standard deviation (SD) of these techniques. Methods: Fifty healthy adults underwent cardiac MRI, with images reconstructed using the AIR Recon DL prototype. This DL approach, which reduces noise and enhances image quality, was applied at three levels and compared to non-DL reconstructions. Results: Analysis focused on the septum to minimize artifacts. For T1 mapping, mean values were 988 ± 50, 981 ± 45, 982 ± 43, and 980 ± 24 ms; for T2 mapping, mean values were 53 ± 5, 54 ± 5, 54 ± 5, and 54 ± 5 ms and for T2* mapping, mean values were 37 ± 5, 37 ± 5, 37 ± 5, and 38 ± 5 ms for no DL and increasing DL levels, respectively. Results showed no significant differences in mean values for any mappings between non-DL and DL reconstructions. However, DL significantly reduced the SD within regions of interest for T1 mapping, enhancing image sharpness and registration accuracy. No significant SD reduction was observed for T2 and T2* mappings. Conclusions: These findings suggest that DL-based reconstructions improve the precision of T1 mapping without affecting mean values, supporting their clinical integration for more accurate cardiac disease diagnosis. Future studies should include patient cohorts and optimized protocols to further validate these findings. Full article

Background: Diagnosing CNS tumors through MRI is limited by significant variability in scanner hardware, acquisition protocols, and intensity characteristics at clinical centers, resulting in substantial domain shifts that lead to diminished reliability for automated models. Methods: We present a Domain-Adaptive MRI Learning Model (DA-MLM) consisting of an adversarially aligned hybrid 3D CNN–transformer encoder with contrastive regularization and covariance-based feature harmonization. Varying sequence MRI inputs ($T1$, $T1ce$, $T2$, and $FLAIR$) were inputted to multi-scale convolutional layers followed by global self-attention to effectively capture localized tumor structure and long-range spatial context, with domain adaptation that harmonizes feature distribution across datasets. Results: On the BraTS 2020 dataset, we found DA-MLM achieved 94.8% accuracy, 93.6% macro-F1, and 96.2% AUC, improving upon previously established benchmarks by 2–4%. DA-MLM also attained Dice score segmentation of 93.1% (WT), 91.4% (TC), and 89.5% (ET), improving upon 2–3.5% for CNN and transformer methods. On the REMBRANDT dataset, DA-MLM achieved 92.3% accuracy with segmentation improvements of 3–7% over existing U-Net and expert annotations. Robustness testing indicated 40–60% less degradation under noise, contrast shift, and motion artifacts, and synthetic shifts in scanner location showed negligible performance impairment (<0.06). Cross-domain evaluation also demonstrated 5–11% less degradation than existing methods. Conclusions: In summary, DA-MLM demonstrates improved accuracy, segmentation fidelity, and robustness to perturbations, as well as strong cross-domain generalization indicating the suitability for deployment in multicenter MRI applications where variation in imaging performance is unavoidable. Full article

Background/Objectives: Holocord astrocytomas are exceptionally rare intramedullary tumors, especially in adults, and often present with extensive longitudinal growth. Because only a small number of cases have been described, management strategies remain insufficiently defined. This report presents an adult patient treated with a staged surgical approach and provides an updated review of the literature. Methods: A 31-year-old male presented with progressive paraparesis, sensory deficits, and sphincter dysfunction. MRI demonstrated an intramedullary tumor extending from T3 to the conus medullaris. The patient underwent a planned two-stage resection with intraoperative neurophysiological monitoring. Histopathological and DNA-methylation analyses were performed. Additionally, a systematic review of previously reported holocord astrocytoma cases was conducted. Results: The two-stage surgical strategy enabled extensive debulking across multiple spinal segments while preserving neurological function. The patient demonstrated marked postoperative improvement, including restoration of sphincter control, improved motor function, and better mobility. Histopathological analyses confirmed a high-grade astrocytoma with piloid features. The literature review identified 28 previously reported cases, including only 5 in adults. Reported neurological outcomes across adult cases are variable, reflecting the heterogeneity and rarity of this tumor entity. Conclusions: Holocord astrocytomas in adults are extremely rare and pose particular diagnostic and therapeutic challenges. This case demonstrates that a carefully planned, staged surgical approach can achieve meaningful neurological recovery, even in patients presenting with severe preoperative deficits. The report expands the limited body of evidence available for adult holocord astrocytomas and may support future management strategies. Full article

This article presents a systematic review on methods for quantifying three-dimensional, time-resolved (3D+t) deformation and motion of human arteries from Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched Scopus, Web of Science, IEEE Xplore, Google Scholar, and PubMed on 19 December 2025 for in vivo, patient-specific CT or MRI studies reporting motion or deformation of large human arteries. We included studies that quantified arterial deformation or motion tracking and excluded non-vascular tissues, in vitro or purely computational work. Thirty-five studies were included in the qualitative synthesis; most were small, single-centre observational cohorts. Articles were analysed qualitatively, and results were synthesised narratively. Across the 35 studies, the most common segmentation approaches are active contours and threshold, while temporal motion is tracked using either voxel registration or surface methods. These kinematic data are used to compute metrics such as circumferential and longitudinal strain, distensibility, and curvature. Several studies also employ inverse methods to estimate wall stiffness. The findings consistently show that arterial strain decreases with age (on the order of 20% per decade in some cases) and in the presence of disease, that stiffness correlates with geometric remodelling, and that deformation is spatially heterogeneous. However, insufficient data prevents meaningful comparison across methods. Full article

Brain magnetic resonance imaging (MRI) is highly susceptible to motion artifacts that degrade fine structural details and undermine quantitative analysis. Conventional U-Net-based deep learning approaches for motion artifact reduction typically operate only in the image domain and are often trained on data with simplified motion patterns, thereby limiting physical plausibility and generalization. We propose Sim-DDNet, a simulation-data-based dual-domain network that combines k-space-based motion simulation with a joint image-k-space reconstruction architecture. Motion-corrupted data were generated from T2-weighted Alzheimer’s Disease Neuroimaging Initiative brain MR scans using a k-space replacement scheme with three to five random rotational and translational events per volume, yielding 69,283 paired samples (49,852/6969/12,462 for training/validation/testing). Sim-DDNet integrates a real-valued U-Net-like image branch and a complex-valued k-space branch using cross attention, FiLM-based feature modulation, soft data consistency, and composite loss comprising L1, structural similarity index measure (SSIM), perceptual, and k-space-weighted terms. On the independent test set, Sim-DDNet achieved a peak signal-to-noise ratio of 31.05 dB, SSIM of 0.85, and gradient magnitude similarity deviation of 0.077, consistently outperforming U-Net and U-Net++ across all three metrics while producing less blurring, fewer residual ghost/streak artifacts, and reduced hallucination of non-existent structures. These results indicate that dual-domain, data-consistency-aware learning, which explicitly exploits k-space information, is a promising approach for physically plausible motion artifact correction in brain MRI. Full article

Background/Objectives: Preoperative differentiation between oral squamous cell carcinoma (SCC) and minor salivary gland carcinoma (SGC) remains clinically challenging due to overlapping imaging characteristics. This study aimed to develop a diagnostic model based on quantitative dynamic contrast-enhanced MRI (qDCE-MRI) parameters to distinguish SCC from SGC prior to surgery. Methods: Patients with histopathologic confirmed SCC or minor SGC who underwent preoperative 3.0T qDCE-MRI were recruited. Clinical characteristics and pharmacokinetic parameters, including volume transfer constant (K^trans), reverse reflux rate constant (K_ep), volume fraction of extravascular extracellular space (V_e), plasma volume fraction (V_p), time to peak (TTP), maximum concentration (MAXConc), maximal slope (MAXSlope), and area under the concentration-time curve (AUCt), along with the apparent diffusion coefficient (ADC), were extracted. Univariate and multivariable logistic regression analyses were performed to identify independent discriminators. Diagnostic performance was assessed using receiver operating characteristic analysis, and model comparisons were conducted with the DeLong test. Interobserver agreement was evaluated using intraclass correlation coefficients (ICC). Results: All qDCE-MRI parameters demonstrated excellent interobserver agreement (ICC range, 0.82–0.94). Multivariable analysis identified K_ep (OR = 2620.172, p = 0.001), maximal slope (OR = 1.715, p = 0.024), and tumor location (OR = 5.561, p = 0.027) as independent predictors. The qDCE-MRI model achieved superior diagnostic performance compared with the clinical model (AUC: 0.945 vs. 0.747; p = 0.012). Conclusions: A qDCE-MRI–based model incorporating K_ep and MAXSlope was shown to provide excellent accuracy for preoperative differentiation between oral SCC and minor SGC. Full article

Background/Objectives: Trace metals, including copper (Cu) and zinc, are associated with the development and prognosis of hepatocellular carcinoma (HCC). However, their interference with magnetic resonance imaging (MRI) limits their use as potential biomarkers. This study investigated the usefulness of Synchrotron Radiation–excited X-ray Fluorescence (SR-XRF) imaging in studying the distribution of trace metals in HCC. Methods: This case–control study analyzed 33 specimens from 32 patients with HCC who underwent surgical resection (n = 29) or biopsy (n = 3) at Kobe University Hospital between December 1999 and November 2002. The findings of SR-XRF were compared with those of MRI and histopathology. Results: SR-XRF provided two-dimensional mapping of trace metal distribution with high spatial resolution (1.0 µm). The mean tumor-to-liver ratio (TLR) of Cu content was significantly higher in well-differentiated HCCs than in moderately and poorly differentiated HCCs (p < 0.05). Moreover, the mean TLRs of Cu content were significantly higher in high-intensity lesions than in iso- or low-intensity lesions on T1-weighted imaging (p < 0.05). Conclusions: This study supports previous evidence of the involvement of Cu in HCC development, suggesting its potential as a clinical biomarker for diagnosis and disease progression. Additionally, the results demonstrate that SR-XRF has potential for clinical application due to its ability to map trace metal distribution at high resolution. These findings suggest, rather than demonstrate, the association among Cu accumulation, tumor differentiation, and MRI signal characteristics. Full article