Search Results (923)

Background: Microstructural abnormalities of the corpus callosum (CC) are a consistent finding in schizophrenia, yet conventional diffusion tensor imaging (DTI) metrics provide limited biological specificity. Neurite orientation dispersion and density imaging (NODDI) can disentangle the neurite density index (NDI) and the orientation dispersion index (ODI), providing indirect, model-based markers of white matter microstructure in vivo. Methods: We applied NODDI to diffusion-weighted MRI data in patients with first-episode schizophrenia (FES) and matched healthy controls (HCs). The CC was used as a mask and subdivided into the genu (GCC), body (BCC), and splenium (SCC). Group differences in z-scores of the NDI and ODI were assessed using voxel-wise statistics within the CC and region of interest (ROI) analyses in the GCC, BCC, and SCC, controlling for age and sex. Associations between NODDI metrics and clinical symptoms were examined using the Positive and Negative Syndrome Scale (PANSS). Results: FES patients showed a significantly increased ODI in portions of the GCC, BCC, and SCC, as well as region-specific NDI alterations, with decreased NDI in parts of the SCC and increased NDI in sub-regions of the GCC/BCC (voxel-wise p < 0.05, FWE-corrected). ROI analyses confirmed a significant reduction in NDI z-scores in the SCC in FES patients compared with HCs (p = 0.009), whereas the ODI z-scores in the SCC did not differ significantly between groups (p = 0.124). Despite the absence of group-level ODI differences in the SCC, the SCC ODI was positively correlated with PANSS negative symptom scores in FES patients (r = 0.554, p = 0.002) and was also positively correlated with PANSS total scores in FES (r = 0.457, p = 0.014). This association remained significant in the region of the SCC after regressing out NDI from ODI (residual z_ODI), which was correlated with PANSS negative scores (r = 0.503, p = 0.006) and PANSS total scores (r = 0.474, p = 0.011), and the ODI/NDI ratio in the SCC was also correlated with negative symptom severity (r = 0.457, p = 0.014). Conclusions: Our findings suggest that, in the SCC, negative symptoms in schizophrenia are linked to altered neurite orientation dispersion under conditions of reduced neurite density. The dissociation between group-level NDI and ODI effects and their distinct relationship with psychopathology highlights the value of composite microstructural indices (e.g., residual z_ODI, ODI/NDI) for capturing clinically relevant white matter abnormalities. Full article

Background: Arginine and related amino acids are widely used to suppress protein aggregation, thereby affecting stability, manufacturability, and therapeutic performance. However, their effectiveness remains limited, necessitating the exploration of alternative strategies. Previous studies have shown that N-acetyl-L-arginine (NA-Arg) can improve protein stability; however, the potential of other N-acetylated amino acids has not been fully explored. Methods: This study aimed to investigate the effects of multiple N-acetylated amino acids as alternative excipients on aggregation, colloidal stability, and viscosity in intravenous immunoglobulin (IVIG) formulations. Dynamic light scattering (DLS) was used to evaluate diffusion behavior and aggregation tendencies, while complementary analyses were performed using size-exclusion chromatography (SEC) and flow-imaging microscopy (FI). Results: Overall, N-acetylation of amino acids improved colloidal stability, shifting the kD values from −5.87 to 6.83 mL/g for arginine and from −8.17 to 16.22 mL/g for histidine, and increased the aggregation onset temperature (Tagg) to above 60 °C. Among the tested compounds, N-acetyl-L-histidine (NA-His) showed the most favorable results, increasing the monomer proportion by approximately 4%, reducing high-molecular-weight species to below 2%, and producing a greater than 10-fold decrease in subvisible particles relative to histidine hydrochloride after 5 days of agitation. At 50 mM, both NA-His and NA-Arg reduced the viscosity of highly concentrated 200 mg/mL IVIG formulations, with NA-His exhibiting the lowest viscosity (7.24 ± 0.12 mPa·s). Protein–protein interaction and surface charge analyses indicated improved colloidal stability relative to parent amino acids, attributable to the presence of the acetyl group. Conclusions: These findings support the potential of N-acetylation as a strategy to modulate interaction-driven instability and suggest NA-His as a promising candidate excipient for stabilizing highly concentrated therapeutic proteins at acidic pH. Full article

Hepatic hemangiomas are the most common benign liver tumors and are typically small and asymptomatic; however, pedunculated and exophytic variants are extremely rare and may mimic extrahepatic lesions on imaging, posing a potential diagnostic challenge. The aim of this study was to describe the multimodal imaging features of a pedunculated hepatic hemangioma arising from the left triangular ligament and to review the available literature with particular attention to MRI findings and diagnostic considerations. A 52-year-old man underwent contrast-enhanced thoracoabdominal CT for unrelated symptoms, which incidentally revealed a pedunculated hepatic lesion. Further evaluation was performed with multiparametric MRI at 1.5T, including diffusion-weighted imaging and dynamic contrast-enhanced sequences. A review of the English-language literature published up to 2025 focusing on pedunculated and exophytic hepatic hemangiomas was also conducted. CT and MRI demonstrated imaging features consistent with hepatic hemangioma, including peripheral nodular enhancement with progressive centripetal fill-in and marked T2 hyperintensity. Multiplanar MRI depicted a thin vascular pedicle connecting the lesion to the hepatic capsule, supporting its hepatic origin. Fewer than approximately 30 well-documented cases have been reported in the English literature. Recognition of these imaging findings may facilitate correct diagnosis and help avoid unnecessary invasive procedures. Full article

The visualization of embryonic and fetal brain development at mesoscopic resolution represents a critical frontier in developmental neuroscience. This review presents advances in high-field magnetic resonance imaging (HF-MRI) that achieve unprecedented spatial resolution in ex vivo human embryonic and fetal brain specimens. This mesoscopic imaging capability bridges the gap between conventional clinical MRI and histological microscopy, enabling three-dimensional visualization of transient developmental structures including cortical lamination, ganglionic eminences, and emerging white matter pathways. We review the technical foundations of HF-MRI, present methodological advances that enable mesoscopic resolution, demonstrate applications across gestation, and discuss validation through histological correlation. The integration of multimodal imaging approaches—including T1-weighted, T2-weighted, T2*-weighted, diffusion tensor imaging, and quantitative relaxometry—provides comprehensive characterization of tissue microstructure and connectivity during critical periods of neurodevelopment. These advances offer transformative potential for understanding normal brain development, identifying early markers of neurodevelopmental disorders, and establishing high-resolution atlases of human prenatal neuroanatomy. Full article

Background: Prostate cancer (PCa) is one of the most prevalent malignancies in men, and active surveillance (AS) is the recommended management strategy for low- and favourable intermediate-risk disease. Predicting which patients will progress during AS remains a clinical challenge. MRI-derived radiomics has shown promise for risk stratification, but conventional machine learning approaches treat radiomic features as independent variables and may not capture the complex inter-feature dependencies within imaging data. This study evaluates the application of Synolitic Graph Neural Networks (SGNNs) to baseline MRI-derived radiomic features for predicting prostate cancer progression on active surveillance. Methods: We studied 343 AS patients (73 progressors, 270 non-progressors) from a single-centre cohort prospectively enrolled between 2013 and 2019 and retrospectively analysed. Seventy-two radiomic features were extracted from baseline 3T MRI (T2-weighted imaging and apparent diffusion coefficient maps), together with three clinical variables (prostate volume, PSA, PSA density). The SGNN pipeline transformed each patient’s feature profile into a weighted graph encoding pairwise feature relationships via logistic regression classifiers trained within each cross-validation fold. GCN and GATv2 architectures were evaluated with multiple sparsification strategies and compared against Gradient Boosting, SVM, Random Forest, and logistic regression using 5-fold stratified cross-validation. Results: Among conventional methods, Gradient Boosting achieved the highest ROC-AUC (0.634 ± 0.080). The SGNN pipeline with GATv2, confidence-based sparsification (p = 0.8), and extended node features incorporating graph centrality measures achieved the best performance (ROC-AUC = 0.699 ± 0.044), an absolute improvement of 0.065 over the best conventional method. The addition of topological node features consistently improved performance by 3–5% across configurations. GATv2 outperformed GCN in matched settings. Conclusions: As a proof of concept, the SGNN framework achieved the highest mean ROC-AUC among the evaluated single-timepoint approaches, though results require confirmation in independent external cohorts. By encoding inter-feature relationships as patient-specific graphs, SGNN offers a complementary modelling paradigm for radiomic data in clinical oncology. Future work should incorporate longitudinal data and graph explainability methods. Full article

Building reliable vehicle detection models for intelligent transportation systems calls for large, well-annotated datasets—yet gathering and labelling real traffic data remains both costly and labour-intensive. This paper introduces Traffic Synth, an automated pipeline that generates synthetic training datasets by altering real traffic camera images rather than constructing entirely artificial scenes. The system begins by detecting vehicles through instance segmentation and removing them from the frame. It then places new vehicles directly into the cleared regions using diffusion-based inpainting, all while retaining the original road layout, lighting, and camera perspective. Doing so preserves the realistic scene context while broadening the visual variety of vehicles in the dataset. To ensure that the resulting traffic looks physically plausible, we incorporate a lane-aware prompting mechanism that matches each vehicle’s orientation to the direction of travel as seen from the camera. The system further draws on a weighted vehicle brand database that mirrors the makes and colours commonly found on European roads to better match actual deployment conditions. Class-specific mask processing—involving anisotropic scaling and relative dilation—rounds out the pipeline by improving generation quality across different vehicle size categories. The final output is a set of images with automatically generated annotations in a standard object detection format. Full article

Background and Purpose: Neurological injury remains a major complication of pediatric cardiac surgery and is closely related to alterations in cerebral blood flow during extracorporeal circulation (ECC). However, the real-time assessment of cerebral perfusion under these conditions has been limited by the lack of magnetic resonance (MR)-compatible perfusion systems. The aim of this pilot feasibility study was to establish a porcine model enabling simultaneous cardiopulmonary bypass (CPB) and real-time MR-based assessment of cerebral blood flow during simulated pediatric cardiac surgery. Methods: We conducted a pilot study on 11 Duroc pigs (14.6 ± 1.4 kg BW), designed in iterative cycles. The experimental setup included an MR-conditional heart-lung machine and a surgical protocol closely mimicking pediatric cardiac surgery. After the initiation of CPB and hemodynamic stabilization, animals were cooled to target temperatures (20 °C or 28 °C) depending on the perfusion strategy. Structural and functional MRI, including phase-contrast imaging, arterial spin labeling, diffusion-weighted imaging, and MR spectroscopy, were performed during cooling and rewarming. Procedural feasibility, technical challenges, and optimization strategies were systematically documented. Results: The study successfully established a reproducible porcine model enabling MR imaging during extracorporeal circulation. Key technical challenges, including vascular access, cannulation of the ascending aorta, and blood volume management, were identified and addressed through the iterative refinement of the surgical and perfusion protocols. The use of the Seldinger technique significantly improved cannulation safety and reduced blood loss. Stable CPB conditions and target hypothermic temperatures were achieved in successfully cannulated animals. MRI acquisition during CPB was feasible, providing simultaneous structural and functional assessment of cerebral perfusion. Representative imaging data demonstrate the capability of the model to capture cerebral hemodynamics in real time. Conclusions: This pilot study establishes a novel MR-compatible porcine model for the real-time assessment of cerebral blood flow during extracorporeal circulation. The platform provides a robust foundation for future quantitative investigations of cerebral perfusion, mechanisms of brain injury, and neuroprotective strategies in pediatric cardiac surgery. Full article

Background and Purpose: Complete angiographic reperfusion (TICI 3) is considered the optimal procedural endpoint of mechanical thrombectomy (MT) in acute ischemic stroke. However, new diffusion-weighted imaging (DWI) lesions are frequently observed despite apparent angiographic success. We aimed to investigate the incidence, morphological patterns, and clinical relevance of these lesions in a strictly defined TICI 3 cohort. Methods: In this retrospective single-center study, 89 patients with anterior circulation large-vessel occlusion (LVO) who achieved true TICI 3 were analyzed. Baseline and follow-up Magnetic Resonance Imaging (MRI) within 48 h were systematically compared using paired diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps to identify new lesions. Lesions were classified according to morphology and distribution. Stroke etiology was assessed using TOAST criteria. Functional outcomes were evaluated using the 90-day modified Rankin Scale (mRS) with the Rankin Focused Assessment. Results: New DWI lesions were detected in 28 of 89 patients (31.5%). The predominant pattern was millimetric cortical foci (85.7%), most frequently ipsilateral to the recanalized vessel (78.6%), with fewer contralateral (14.3%) and bilateral (7.1%) lesions. Territorial infarcts and isolated basal ganglia infarcts were each identified in 14.3% of patients, with some overlap between categories. No significant differences were observed between patients with and without new lesions regarding baseline characteristics or procedural metrics (all p > 0.05). Importantly, the presence of new DWI lesions was not associated with 90-day functional outcome (p = 0.930) or survival (p = 0.613). Conclusions: New DWI lesions are common even after complete angiographic reperfusion, highlighting a persistent dissociation between macrovascular success and tissue-level integrity. Although predominantly small and clinically silent in the short term, these findings underscore the limitations of angiographic endpoints alone and support the need for strategies targeting microvascular protection and prevention of distal embolization. Full article

Parkinson’s disease (PD) may benefit from non-pharmacological motor–cognitive rehabilitation, but sensitive neuroimaging markers of training-related brain changes remain limited. This study investigated whether 4 weeks of daily Quadrato Motor Training (QMT) modulate resting-state functional connectivity (FC) in PD and secondarily explored whether whole-brain radiomic features derived from T1-weighted and fractional anisotropy (FA) images could detect pre–post differences over this short intervention interval. Fifty patients with idiopathic PD were randomized to QMT or a SHAM repetitive stepping condition, and 48 completed the protocol (25 SHAM, 23 QMT). MRI was acquired at baseline and after 4 weeks and included resting-state fMRI, 3D T1-weighted imaging, and diffusion-derived FA maps. Resting-state fMRI was analyzed using independent component analysis and dual regression, whereas an IBSI-compliant radiomics workflow and machine-learning models were used for exploratory scan-level classification. Compared with baseline, the SHAM group showed reduced synchronization across several resting-state networks, whereas the QMT group showed increased synchronization in the right sensorimotor and frontoparietal networks and no significant reductions. Between-group analyses showed lower delta-FC in SHAM than QMT in the cerebellar and sensorimotor networks. In contrast, radiomics showed limited discrimination between pre- and post-QMT scans; the best model achieved a ROC-AUC of 0.65 with near-chance accuracy, and no selected predictor remained significant after multiple-comparison correction. These findings suggest that QMT may support short-term functional network stability or task-relevant reorganization in PD relative to the SHAM condition, whereas whole-brain structural radiomics appears less sensitive for detecting early training-related effects in this setting. Full article

As modern urban systems advance, video surveillance has become indispensable for ensuring high-quality urban development. Nighttime images acquired in urban monitoring scenarios are often degraded by haze and non-uniform illumination, resulting in reduced visibility, color distortion, and blurred structural boundaries. To address these issues, this paper proposes a nighttime image dehazing framework that combines mixed-norm variational atmospheric-light estimation with adaptive boundary-constrained transmission refinement. Specifically, an $L_2-L_{\mathrm{p}}$ mixed-norm regularization model is introduced to improve atmospheric-light estimation under complex nighttime illumination and suppress halo diffusion and color distortion around strong light sources. In addition, an adaptive boundary-constrained transmission refinement strategy with weighted soft-threshold shrinkage is developed to reduce residual artifacts while preserving structural edges. Experimental results on synthetic and real nighttime haze datasets demonstrate that the proposed method consistently outperforms representative state-of-the-art methods in both visual quality and quantitative metrics, showing superior robustness and restoration performance for nighttime urban monitoring applications. Full article

Background/Objectives: Magnetic nanoparticles have emerged as powerful tools for biomedical imaging, targeted drug delivery, and hyperthermia therapy. Magnetic particle imaging (MPI) is among the most promising technologies built around its properties: a radiation-free, quantitative tomographic modality that detects superparamagnetic iron oxide nanoparticles (SPIONs) directly against a biologically silent background. This review synthesizes MPI’s physical principles, nanoparticle design strategies, and preclinical applications within the broader landscape of magnetic material engineering for biomedical use. Methods: A systematic review was conducted covering MPI signal generation and image reconstruction, nanoparticle core synthesis and surface coating approaches, and preclinical applications, spanning cell tracking, oncological imaging, vascular perfusion, neuroimaging, and MPI-guided theranostics. Studies were selected to provide quantitative benchmarks and direct comparisons with competing modalities where available. Results: MPI delivers signal-to-background ratios above 1000:1, iron-mass linearity at R² ≥ 0.99, regardless of tissue depth, and acquisition rates up to 46 volumes per second. Tracer architecture—encompassing single-core particles, multicore nanoflowers, and stimuli-responsive cluster designs—is the primary determinant of sensitivity, environmental robustness, and theranostic capability. Preclinical results include detection of cell populations in the low thousands, earlier ischaemia identification than diffusion-weighted MRI, real-time drug release quantification, and spatially confined tumour hyperthermia. Three translational bottlenecks are identified: the absence of a clinically approved tracer with optimal relaxation dynamics, hardware performance losses when scaling to human-bore systems, and overestimation of passive tumour accumulation in murine models. Conclusions: MPI illustrates how progress in magnetic material design directly expands clinical imaging and theranostic possibilities. Successful translation will require indication-driven, interdisciplinary development that integrates materials science, scanner engineering, and regulatory strategy in parallel. Full article

Background and Objectives: Prostate cancer is the second most common cancer in men worldwide, with 1,466,680 new cases and 396,792 deaths reported in 2022. Accurate preoperative grading is critical, as the grade assessed on biopsy cores may be underestimated compared to radical prostatectomy specimens. The aim of this study was to assess the ability of quantitative diffusion parameters derived by the standard monoexponential model (ADC—apparent diffusion coefficient) and kurtosis model (Dapp—apparent diffusion coefficient corrected for non-Gausion behavior and K-kurtosis) to predict Gleason Grade Group (GG) upgrading from transrectal ultrasound-guided (TRUS) biopsy to radical prostatectomy within each GG. Materials and Methods: This retrospective study included 128 patients with prostate cancer who underwent systematic TRUS biopsies and multiparametric magnetic resonance imaging (mpMRI) at 3T before prostatectomies between 2017 and 2021. Mean values of quantitative diffusion parameters (ADC, Dapp, K) were compared between upgraded and non-upgraded cohorts within each Grade Group obtained at biopsy. Results: Significant differences in ADC and K values were found between upgraded and non-upgraded lesions in GG1 and GG2 cohorts at biopsy, with lower ADCs and higher K values indicating a higher likelihood of upgrading. In GG1, ADC demonstrated an AUC of 0.762 (p < 0.05) and K an AUC of 0.846 (p < 0.05). In GG2, ADC showed an AUC of 0.814 (p < 0.001) and K an AUC of 0.755 (p < 0.001). No significant differences were observed in GG3 and GG4 cohorts. Conclusions: Quantitative diffusion parameters—particularly ADC and kurtosis (K)—demonstrated significant predictive value for Grade Group upgrading in patients with biopsy-proven GG1 (AUC: K = 0.846, ADC = 0.762) and GG2 (AUC: ADC = 0.814, K = 0.755, D = 0.810) prostate cancer. These findings suggest that incorporating quantitative DWI parameters into preoperative assessments may improve risk stratification and support clinical decision-making, particularly regarding the selection of patients for active surveillance. Validation in larger, multicenter cohorts is warranted. Full article

Background/Objectives: To evaluate and compare the qualitative and quantitative image performance of multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) against conventional single-shot (ss-DWI) and high-resolution single-shot (HR-ssDWI) sequences in patients with esophageal cancer. Methods: Twenty patients who underwent esophagus MRI, including ss-DWI, HR-ssDWI and MUSE-DWI, were retrospectively enrolled. Image quality, esophageal contour, lesion conspicuity and image distortion were independently graded by two radiologists using a five-point scale and compared between the three sequences. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of esophageal tissue were measured and compared between the three sequences. Results: After Bonferroni correction (p < 0.017), MUSE-DWI had significantly higher scores than HR-ssDWI in image quality, esophageal contour delineation and lesion conspicuity, and all three sequences had statistically significant differences in image distortion scores with MUSE-DWI performing the best. Quantitative analysis revealed that MUSE-DWI had the highest SNR and CNR values; significant differences were found in SNR between ss-DWI and HR-ssDWI (p < 0.001), and in both SNR and CNR between HR-ssDWI and MUSE-DWI (p < 0.001), while no significant differences were observed in SNR and CNR between ss-DWI and MUSE-DWI (p > 0.017). Conclusions: MUSE-DWI outperforms ss-DWI and HR-ssDWI in reducing image distortion, with comparable quantitative image quality metrics to ss-DWI. It represents a valuable optimized DWI technique for esophageal clinical imaging. Full article

Background: Breast cancer brain metastases (BCBMs) are clinically challenging, and treatment decisions are influenced by tumor biology. Because receptor profiles may differ between primary breast tumors and brain metastases and brain biopsy may be impractical, non-invasive imaging biomarkers may provide useful biologic correlates. We evaluated whether diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC) histogram metrics from BCBM were associated with primary tumor estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status; the Ki-67 proliferation index; and luminal status. Methods: This retrospective exploratory single-center study included 72 adults with BCBM who underwent standardized 1.5T brain magnetic resonance imaging. The largest lesion in each patient was segmented on ADC maps in FireVoxel. ADC histogram features, including percentiles, were extracted. Using primary tumor biomarker status as the reference, candidate metrics were screened by univariable logistic regression. Parsimonious multivariable models included age, log-transformed lesion volume, and a single selected ADC percentile scaled by ×10. Discriminatory performance was assessed using area under the receiver operating characteristic curve (AUC); thresholds were derived with the Youden index. No external validation was performed. Results: Low-percentile ADC metrics were associated with ER positivity, PR positivity, and luminal disease, whereas no meaningful ADC histogram discrimination was observed for HER2. In multivariable models, ADC10×10 predicted ER positivity (odds ratio [OR] 0.441; AUC 0.847) and PR positivity (OR 0.478; AUC 0.819). Ki-67 positivity was best predicted by ADC75×10 (OR 3.095; AUC 0.905), although this finding should be interpreted cautiously. Luminal status (non-luminal vs. luminal) was predicted by ADC10×10 (OR 2.251; AUC 0.832). Conclusions: ADC histogram analysis from DWI in BCBM showed exploratory associations with primary tumor hormone receptor status and luminal subtype, but not HER2. These findings support ADC histogram features as candidate imaging biomarkers, but the Ki-67 result and all model performance estimates require cautious interpretation and independent external validation in multicenter cohorts. Full article

Background/Objectives: Breast MRI surveillance for women with a personal history of breast cancer (PHBC) is often limited by costs and acquisition times. This study aims to identify the optimal abbreviated breast MR (ABMR) protocol for this population by assessing the diagnostic performance of different sequence additions. Methods: This retrospective study included 1002 women with PHBC who underwent postoperative breast MRI with ultrafast sequences. Propensity score matching using 12 variables yielded recurrence (n = 21) and nonrecurrence (n = 42) groups with balanced characteristics. Four ABMR protocols were simulated by sequentially combining sequences: Step 1 (FAST protocol) included precontrast T1-weighted imaging (T1WI), early-phase T1WI, and subtracted maximal intensity projection (MIP). Step 2 added ultrafast MIP; Step 3 incorporated delayed-phase T1WI; and Step 4 included T2WI and diffusion weighted imaging (DWI). Three expert breast radiologists independently reviewed MRIs. Sensitivity, specificity, accuracy, and area under the curve (AUC) were assessed. Results: Sensitivity, specificity, and accuracy for ABMR protocols ranged from 76.2% to 90.5%, 88.1% to 92.9%, and 85.7% to 90.5%, respectively. The FAST protocol alone provided reliable performance (sensitivity: 81%; specificity: 88.1–90.5%; accuracy: 85.7–87.3%). Additional sequences yielded modest improvements, but no statistically significant differences were observed across all 3 readers (p > 0.05). ABMR protocols demonstrated equivalent diagnostic performance for PHBC surveillance. Conclusions: The FAST protocol alone provided reliable results, indicating its potential as a primary ABMR protocol. While additional sequences slightly improved specificity, they did not significantly enhance diagnostic accuracy. Full article