Neuroradiology最新文献

A 43-year-old male patient presented with monoparesis in his left leg, which had persisted for one year, then progressed to spastic dysarthria, tetraparesis, wide-based gait, muscle atrophy, weakness, fasciculations, and signs of pyramidal signs in all limbs. Brain MRI findings revealed hyperintensities on T2/FLAIR and diffusion-weighted imaging (DWI) along the corticospinal tracts, extending from the corona radiata and internal capsules to the brainstem, the "bright tongue sign" and the "wine glass sign,". This case highlights the classic findings in amyotrophic lateral sclerosis, which was confirmed by electroneuromyography.

Purpose: Accurate preoperative assessment of internal carotid artery (ICA) invasion is crucial in managing glomus jugulare tumors. This study evaluated the efficacy of contrast-enhanced 3D BRAin VOlume (BRAVO) imaging compared to enhanced fast spin-echo (FSE) T1-weighted imaging and enhanced computed tomography (CT).

Methods: Retrospective analysis was performed on imaging data from surgically confirmed glomus jugulare tumors, including temporal bone enhanced BRAVO, enhanced T1-weighted FSE, and temporal bone enhanced CT sequences. ICA encasement and stenosis by tumor were graded and compared based on intraoperative assessment. According to Fisch criteria, the preoperative image C-type based on BRAVO, FSE and CT were separately and compared with surgical C-type (gold standard).

Results: Among 21 patients, For image Fisch C-type, BRAVO showed excellent agreement with surgical C-type (κ = 1.000, P < 0.001), outperforming enhanced FSE (κ = 0.561) and CT (κ = 0.702). For ICA encasement, BRAVO had moderate agreement (κ = 0.513), slightly better than enhanced FSE (κ = 0.431) but inferior to enhanced CT (κ = 0.648). For ICA stenosis, BRAVO (κ = 0.588) surpassed enhanced FSE (κ = 0.339) but was less accurate than enhanced CT (κ = 0.716).

Conclusion: Enhanced BRAVO and temporal bone enhanced CT are complementary for assessing ICA involvement in glomus jugulare tumors, offering superior accuracy over conventional FSE imaging.

Purpose: Middle meningeal artery (MMA) embolization is an adjunct or alternative to surgery for chronic subdural hematoma (cSDH), but data on coil-forward strategies remain limited. The purpose of this study is to evaluate safety and outcomes after MMA embolization using Optiblock coils engineered for efficient mechanical vessel occlusion.

Methods: Clinical data was extracted for a retrospective cohort of adults treated from February 2023 to December 2024 with ≥ 1 Optiblock coil; adjunctive embolic agent allowed. Symptoms, modified Rankin Scale (mRS), maximal SDH thickness, and midline shift were assessed at serial timepoints. McNemar's test and the Friedman test were used for longitudinal comparisons, with α = 0.05.

Results: Thirty-one patients underwent embolization (mean age 74.0; 84% male). Procedures used a mean of 1.28 coils with an average total coil length of 27.2 cm. The most common Optiblock coil used was 3.5 mm x 20 cm. Embospheres (MERIT medical) were used as an adjunct in 87% of cases. Mean SDH thickness decreased from 14.8 mm pre-operatively to 8.9, 4.4, and 2.1 mm at ~ 1, 3, and 6 months post-operation (p < 0.05), corresponding to 40%, 71%, and 86% reductions; midline shift decreased concordantly (p < 0.05). mRS improved from 2.2 at baseline to 0.6 and 0.1 at first and last follow-up, respectively (p < 0.001). No periprocedural complications were noted. Interval surgical drainage was performed in 2 patients (6.5%) after initial isolated MMA embolization. Two delayed deaths occurred, both from known cardiovascular conditions unrelated to embolization.

Conclusion: Optiblock-based MMA embolization was associated with low complication and reintervention rates and substantial clinical, functional, and radiographic improvement. These coils offer an efficient alternative and/or adjunct to liquid/particle embolization, particularly in cases where particle or liquid embolization is contraindicated or technically unfeasible.

Purpose: Although the T1w/T2w ratio was introduced as a proxy for myelin, it may reflect broader aspects of brain tissue health. The recently introduced T1w/FLAIR ratio has yet to be evaluated in relation to biological and clinical markers. This study investigates its association with myelin water fraction (MWF), diffusion MRI (dMRI), and cognitive performance, and explores whether alternative weightings of the ratio improve these associations.

Methods: 36 women were retrospectively included from a previous study with neuropsychological testing and 3T MRI. Spearman correlations assessed associations between the T1w/FLAIR ratio and MWF, fractional anisotropy (FA), mean diffusivity (MD), neurite density index (NDI), orientation dispersion index (ODI), and fraction of isotropic diffusion compartment (FISO). Region-specific and overall correlations were evaluated across 36 white matter regions, lesions, total white matter, and cortical and deep gray matter. Associations with attention, memory, and processing speed were examined. Least squares regression was used to derive an optimally weighted ratio based on MWF, and analyses were repeated using this weighted ratio.

Results: Significant correlations between the T1w/FLAIR ratio and MWF and/or dMRI measures were found in six out of 40 regions. Across regions and subjects, the ratio showed weak overall correlations (r < 0.5) with MWF, FA, NDI, and FISO. A significant positive correlation with memory was only observed within lesions. The optimal weighting favored a stronger contribution from the T1w signal but resulted in only slightly stronger correlations.

Conclusion: Our results support the interpretation of T1w/FLAIR ratio as a general marker of tissue health rather than a specific measure for myelin.

This review seeks to provide neuroradiologists and clinicians with an imaging-based pattern recognition framework for primary mitochondrial disorders affecting the central nervous system (CNS). By utilising a comprehensive imaging phenotype approach to CNS mitochondrial disorders, it highlights the wide spectrum of neuroimaging patterns and the complexities they present in clinical settings. Using illustrative case examples, the review demonstrates how imaging acts as a vital bridge between clinical phenotypes and genotypes.

Purpose: To review recent advances in structural MRI post-processing for pediatric drug-resistant epilepsy, with emphasis on artificial intelligence-driven and quantitative techniques, including MELD-Graph, MAP18, FLAT1, and SUPR-FLAIR, and to evaluate their impact on lesion detection, epileptogenic zone localization, and presurgical planning.

Methods: Novel post-processing approaches were examined with respect to their computational foundations, imaging requirements, and diagnostic performance. Techniques employing machine learning, deep learning, voxel-based morphometry, cortical surface projection, and FLAIR/T1 ratio mapping were assessed for their applicability in children and their integration into multimodal evaluation pathways alongside electrophysiology and functional imaging.

Results: Advanced post-processing tools substantially increase sensitivity for detecting subtle cortical abnormalities, particularly in MRI-negative pediatric epilepsy. MELD-Graph identify features of focal cortical dysplasia through automated surface-based analysis and deep neural network classification, achieving notable lesion detection even when conventional MRI findings are normal. MAP18 provides complementary voxel-wise morphometric assessment, improving specificity and benefiting from optimized structural sequences. FLAT1 enhances lesion conspicuity by quantifying FLAIR/T1 signal relationships, while SUPR-FLAIR improves visualization of cortical signal abnormalities through normalized FLAIR intensity projection onto the cortical surface. When incorporated into multimodal diagnostic workflows, these methods refine epileptogenic zone localization, inform individualized surgical strategies, and can reduce reliance on invasive testing.

Conclusion: Advanced structural MRI post-processing is transforming the neuroradiological evaluation of pediatric drug-resistant epilepsy. By revealing subtle cortical abnormalities not visible on conventional imaging, these tools support more precise lesion characterization and surgical planning. Ongoing efforts toward standardization, clinical validation, and workflow integration will be essential to ensure widespread adoption and maximize clinical impact within precision-medicine approaches to pediatric epilepsy.

Background: Pretreatment determination of histological differentiation grade is critical for prognostic evaluation in laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC). This study aimed to develop a contrast-enhanced CT (CECT)-based Vision Transformer (ViT) model for noninvasive evaluation of histological grades in LHSCC.

Methods: In this retrospective multicenter study, a total of 1,648 LHSCC patients who underwent CECT scans were enrolled from three hospitals in this study. Participants were divided into a training cohort (n = 1,239), an internal validation cohort (n = 310) from one hospital, and an external validation cohort (n = 99) from the other two hospitals. The diagnostic model integrates a pre-trained ViT for CECT feature extraction and an XGBoost classifier for prediction. The model's predictive performance was evaluated using the area under the curve (AUC), decision curve analysis (DCA), and calibration curve.

Results: The ViT model achieved AUCs of 0.887 (95%CI: 0.848-0.927) in internal validation and 0.796 (95%CI: 0.693-0.899) in external validation cohorts, significantly outperforming the conventional radiomics model (AUCs: 0.775, 95%CI: 0.714-0.837 and 0.544, 95%CI: 0.388-0.699; p < 0.001 and 0.002, respectively). Clinically, DCA demonstrated superior clinical utility, while calibration curves showed excellent prediction reliability. Gradient-weighted Class Activation Mapping visualization identified CT image regions most influential for the model's predictions, providing interpretability for clinical decision-making.

Conclusion: The ViT-based deep learning model developed in this study using CECT demonstrated excellent predictive performance for histological grading of LHSCC, with promising application for patient prognosis assessment.

Purpose: Guillain-Barré Syndrome (GBS) is an autoimmune disorder causing acute inflammatory polyneuropathy, resulting in muscle weakness. Timely diagnosis is critical to prevent complications such as respiratory failure and long-term disability. Ultrasound imaging of peripheral nerves, specifically assessing nerve cross-sectional area (CSA), has been suggested as a diagnostic tool for GBS. This systematic review aims to evaluate the utility of nerve ultrasound in diagnosing and monitoring GBS.

Methods: A systematic review was conducted following PRISMA guidelines, searching databases including PubMed, Scopus, Web of Science, and Cochrane Library up to December 2024. Studies that used ultrasound to assess peripheral nerve size in GBS patients compared to healthy controls or other neuropathy patients were included. Statistical analysis was conducted using Review Manager 5.4 software.

Results: Out of 848 studies, 25 met the inclusion criteria, with 12 included in the meta-analysis. A total of 528 patients with GBS were included. Ultrasound revealed significant increases in the CSA of cervical, peroneal, median, ulnar, and tibial nerves in GBS patients. Specifically, cervical nerve enlargement (MD: 1.45, P = 0.0008) and peroneal nerve enlargement (Mean Difference (MD): 2.09, P < 0.00001) were notable. Subgroup analysis revealed significant enlargement of the ulnar and tibial nerves across different anatomical regions.

Conclusion: Ultrasound imaging of peripheral nerves, particularly changes in CSA, provides valuable diagnostic insight for GBS, may be helpful in early recognition and intervention. Further studies are needed to establish consistent CSA patterns and improve diagnostic accuracy across various GBS subtypes.