AJNR. American journal of neuroradiology最新文献

Background: The glymphatic system, essential for metabolic waste clearance and brain homeostasis, is vulnerable to disruption following traumatic brain injury (TBI). There is a pressing need for practical and robust methods to assess glymphatic function after TBI, as well as for other neurologic disorders. Diffusion tensor imaging along the perivascular space (DTI-ALPS) might have value in quantifying glymphatic dysfunction.

Purpose: Our aim was to consolidate existing evidence to determine differences in DTI-ALPS values between TBI patients and healthy controls (HCs).

Data sources: All studies utilizing the DTI-ALPS index and reporting its mean and standard deviation in both TBI patients and healthy controls were identified through searches of PubMed, Embase, Scopus, and Web of Science from inception to August 8, 2025.

Study selection: Eleven studies comprising 694 patients with TBI, and 503 HCs were included.

Data analysis: Meta-analysis was conducted using a random-effects model. Standardized mean differences (Hedges' g) were used as the effect size measure. Pooled correlations between DTI-ALPS indices and demographic variables were evaluated. Heterogeneity was assessed using Higgins' I² statistic. Subgroup analyses, meta-regression, and sensitivity analyses were performed to identify potential sources of heterogeneity, and publication bias was examined using funnel plots and Begg's test.

Data synthesis: DTI-ALPS values were found to be significantly reduced in TBI patients compared to HCs (Hedges' g = -0.77; 95% CI: -1.38 to -0.15; I²=93%). DTI-ALPS showed no significant correlation with age or Glasgow Coma Scale scores. Subgroup analyses revealed larger effect sizes in single-shell studies and those with higher methodological rigor. Meta-regression showed a larger decrease in DTI-ALPS values in TBI patients over time (β=-0.01, p=0.03). No substantial publication bias was detected (p=0.12).

Limitations: Our meta-analysis is limited by substantial heterogeneity and the small number of included studies.

Conclusions: TBI is associated with significantly reduced DTI-ALPS values, with more prominent deteriorations over the long term, supporting its potential as a biomarker of glymphatic impairment. However, methodological heterogeneity emphasizes the need for standardized protocols and longitudinal studies to establish clinical utility.

Objectives: 7T MRI enhances lesion detection in epilepsy but is limited by radiofrequency transmission field (B1+) inhomogeneity and long scan times. Recent advancements in dynamic parallel transmission and deep-learning-based reconstructions offer promising solutions. We aimed to optimize an enhanced 7T epilepsy protocol incorporating these innovations and evaluate real-world benefits compared to standard 7T epilepsy protocol.

Materials and methods: We retrospectively compared 40 consecutive brain MRIs acquired using a standard 7T epilepsy protocol to 40 MRIs obtained with an enhanced protocol with dynamic parallel transmission and deep-learning-based k-space reconstructions. Quantitative metrics for comparison included image noise, signal homogeneity (coefficient of variation), and resolution/time trade-offs.

Results: The enhanced protocol demonstrated significant improvements in resolution, scan time, noise levels, and image homogeneity. Edge-enhancing gradient echo and magnetization-prepared rapid gradient echo with 2 inversions sequence exhibited a 57.8% reduction in voxel volume while reducing scan time by 33.0% and improving image homogeneity (P=.002) without a significant change in noise (P=0.09). Deep-learning-based reconstruction of coronal T2 turbo spin echo resulted in a 25.7% reduction in noise (P<.001), and patient-specific B1+ shimming achieved homogeneity comparable to dielectric pads. Sampling perfection with application-optimized contrasts using a different flip angle evolutions fluid-attenuated inversion recovery had reduced noise (P<.001), enhanced homogeneity (P<.001), and halved voxel size while maintaining similar scan time. Deep-learning-based echo planar imaging susceptibility-weighted imaging improved acquisition time by 56.5% with a 20.5% reductionin noise (P=.001). Despite increased resolution and parallel transmission use, overall scan time was less than 25 minutes, half the duration recommended by the 7T Epilepsy Task Force.

Conclusions: Integration of dynamic parallel transmission and deep-learning-based reconstructions enhances image resolution, reduces scan time, and improves image homogeneity, addressing barriers to routine clinical implementation of 7T MRI. These advancements may improve lesion conspicuity and contribute to better outcomes for patients with epilepsy.

Background: Radiologically Isolated Syndrome (RIS) entails incidental Multiple Sclerosis (MS)-like MRI lesions. Longitudinal fMRI could clarify brain-symptom links; however, no longitudinal resting-state fMRI studies in RIS existed until now.

Objectives: Compare 14-month clinical, neuropsychological, and resting-state functional connectivity (FC) trajectories in RIS, MS, and healthy controls (HC), and relate FC change to fatigue.

Methods: Nineteen RIS, 20 MS, and 22 HC completed baseline and 14-month assessments (fatigue, neuropsychology) and 3T MRI (rs-fMRI, 3D T1, FLAIR). FC within canonical networks and the ventral attention network (VAN) seed-to-voxel (CONN) connections were tested with a repeated-measures ANOVA (FWE-corrected). Regression analysis related to FC to fatigue; ROC curves evaluated discrimination.

Results: Fatigue rose in MS but was stable in RIS. VAN connectivity showed opposing trajectories (group × time, p < 0.001): RIS increased within-VAN (and within-DAN vs. HC), whereas MS decreased within-VAN. In MS, VAN connectivity increased with orbitofrontal and striatal regions and decreased with thalamus/caudate (FWE p<0.05). Greater increases in within-VAN and VAN-thalamus/caudate connectivity were predicted to lead to fatigue reduction. A composite VAN metric differentiated RIS from MS (AUC=0.919). Lesion volumes were unchanged.

Conclusions: RIS and MS exhibit divergent, VAN-centered FC trajectories paralleling fatigue evolution. VAN-based longitudinal FC metrics may provide sensitive, noninvasive biomarkers that complement lesion measures in early MS.

Background and purpose: The glymphatic system facilitates perivascular clearance, and its dysfunction has been implicated in neurodegenerative diseases. Diffusion Tensor Imaging Along the Perivascular Space (DTI-ALPS) has been proposed as an indirect approach to assess glymphatic function, but its reliability is debated. The choice of b-value is an aspect of possible improvement. While a b-value of 1000 s/mm² is commonly used, the optimal b-value for DTI-ALPS remains unknown. This study aims to determine the optimal b-value for DTI-ALPS.

Methods: Simulations were conducted to examine how the choice of maximum b-value influences bias, precision, and effect size of the ALPS index. DTI-ALPS was applied in a cohort of 194 participants divided into four groups: healthy controls (n=42), Parkinson's disease patients (n=119), Parkinson's disease dementia patients (n=16), and progressive supranuclear palsy patients (n=17). ALPS indices were calculated by manually placing regions of interest on projection and association fibers in each hemisphere. Group differences in ALPS indices across b-values were analyzed using mixed models.

Results: In vivo, ALPS indices were higher at a b-value of 500 and 250 s/mm² compared to a b-value of 1000 s/mm² in both hemispheres. Simulations indicated a bias-variance trade-off: very low b-values reduced sensitivity and compromised precision, while high b-values improved precision but reduced accuracy. The simulated effect size of the ALPS index peaked at intermediate b-values (≈700 s/mm²). In vivo, ALPS indices were lower in Parkinson's disease dementia and Progressive supranuclear palsy patients compared to healthy controls, though differences varied across b-values.

Conclusions: Both simulations and in vivo results suggest that the commonly used b-value of 1000 s/mm² is not optimal for assessing diffusion in the perivascular spaces. Intermediate b-values at approximately 700 s/mm² appear more suitable. However, further optimization of acquisition parameters is needed.

Background and purpose: Scrub typhus is an endemic zoonosis caused by Orientia tsutsugamushi, presenting with a range of neurological manifestations. Despite its high prevalence in endemic areas and clinical relevance, a systematic description of the neuroimaging patterns remains sparse. This study emphasizes the imaging spectrum with clinic-radiological correlations of neurological manifestations of scrub typhus across three tertiary care centers in South India.

Materials and methods: This retrospective multicenter study included 55 patients with neurological symptoms and serologically confirmed scrub typhus, who underwent MRI between January 2020 and March 2025. Two experienced neuroradiologists reviewed the imaging for patterns, along with available CT imaging. Detailed demographic, clinical, and laboratory data were studied from health records.

Results: MRI abnormalities were found in 46 of the 55 patients (83.6%). Leptomeningeal enhancement was the most common observation (49.1%), primarily affecting the parieto-occipital and cerebellar sulci, and was best appreciated on post-contrast FLAIR. Encephalitic changes were seen in 16.4% with heterogeneous patterns including cortical, basal ganglia, thalamic, hippocampal, ADEM-like, and ANE-like involvement. 12.7% had cerebellitis, 9.1% had multifocal restricted diffusion, 7.3% had white matter hyperintensities, 7.3% had rhombencephalitis, and 5.5% had myelitis. Lacunar/cerebellar infarcts (5.5%), cerebral venous thrombosis (3.6%), and micro haemorrhages (9.1%) were among the vascular manifestations. Cranial nerves were involved in 5.5%. 20/28 patients (71.4%) had CT abnormalities, with diffuse cerebral edema being the most prevalent. Leptomeningeal enhancement frequently occurred with encephalitis and cerebellitis, while myelitis occurred with rhombencephalitis. ASL was performed in 6 patients, demonstrating hyperperfusion in cases of encephalitis and cerebellitis. Follow-up imaging in 7 patients revealed complete resolution of leptomeningeal and cerebellar enhancement, with variable evolution of encephalopathic changes, ranging from complete resolution to gliosis and volume loss.

Conclusions: Scrub typhus neuroinfection demonstrates a broad imaging spectrum, most frequently leptomeningeal enhancement with characteristic parieto-occipital and cerebellar predilection. MRI remains the modality of choice, though CT retains diagnostic value in acute or resource-limited settings. Recognition of these patterns in febrile patients from endemic regions can expedite diagnosis and treatment, preventing neurological sequelae.

Structured reporting in radiology is universally endorsed by the radiology societies, including American Society of Neuroradiology/American Society of Spine Radiology (ASNR/ASSR), Structured reporting offers many advantages including: standardization of reports and simplifying reports for referring providers and researchers to extract meaningful and important information. Furthermore, templates can guide radiologists by providing a "checklist" on necessary items to include in the report which can facilitate patient care and optimize patient management.Despite the known benefits of structured reporting, currently structured reporting of spinal metastasis continues to lack. This is explained by many factors including complexity of spinal metastasis, variability of its appearance based on primaries, multiplicity of lesions/variable extent of disease, and technical differences among MRI acquisition protocols between institutions.In this white paper from the American Society of Spine Radiology Education and Standards, we aim to provide a recommended structured reporting of spinal metastasis highlighting pertinent observations that are needed in reporting metastasis, reflecting relevance of radiology report to recent advances in treatment modalities, discussing advanced and emerging imaging modalities, and finally touching briefly on follow up recommendations and challenges.

Background and purpose: Accurate detection of pituitary microadenomas is critical for the diagnosis and treatment of Cushing's disease (CD). However, conventional MRI often has limited resolution and thick slices, leading to missed lesions and suboptimal surgical planning. This study investigates the diagnostic utility of artificial intelligence-assisted compressed sensing (ACS) applied to conventional anatomical MRI, combined with DCE-MRI using united Compressed Sensing with Radial Acquisition (uCSR), aiming to improve spatial resolution and lesion detection without prolonging scan time, while uCSR enhances temporal resolution and motion robustness in dynamic contrast imaging.

Materials and methods: This prospective study included 61 patients with surgically confirmed Cushing's disease who underwent both conventional and ACS-accelerated MRI sequences, including T2WI, contrast-enhanced T1-weighted imaging (T1WI-C), and delayed FLAIR, along with DCE-MRI using uCSR technique. Image quality assessments and lesion detection rates were compared. Pharmacokinetic parameters (Ktrans, Kep, Ve) derived from DCE were evaluated across lesion types.

Results: A total of 61 patients (median age, 42 years old; 56% female) were included, with 71 lesions identified, including 9 patients with multiple lesions and 2 patients with ectopic lesions. ACS-T1WI-C achieved higher image clarity scores compared with conventional T1WI-C (4.7 ± 0.3 vs 4.1 ± 0.6; P < 0.001) and higher signal-to-noise ratio (SNR, 30.1 ± 3.4 vs 22.3 ± 2.4; P < 0.001). Similarly, ACS-T2WI showed higher contrast-to-noise ratio (CNR, 12.4 ± 3.1 vs 8.5 ± 2.3; P < 0.001). Across all sequences, the combination of ACS-T1WI-C and delayed FLAIR detected all 71 lesions, corresponding to a sensitivity of 94.9% and specificity of 93.5%, significantly higher than conventional sequences (P < 0.001). Interobserver agreement for lesion detection was excellent (κ = 0.91) for ACS sequences. Multiple lesions (14.7%) showed significant pharmacokinetic differences; adrenocorticotropic hormone (ACTH)-secreting adenomas demonstrated significantly lower Ktrans and Kep compared with Rathke's cysts and non-functional adenomas (P < 0.01).

Conclusion: ACS significantly improves image quality and lesion detection in CD, providing high-resolution imaging without extending acquisition time. uCSR-based DCE-MRI further aids lesion-type differentiation, contributing to more accurate preoperative localization and diagnosis.

The diagnostic yield of CTP for cerebral venous thrombosis (CVT) is uncertain. We aimed to estimate the sensitivity, specificity, predictive values and area under the curve (AUC) of CTP for CVT diagnosis, hypothesizing that CTP review would increase CVT diagnosis accuracy. Retrospective analysis of patients with stroke-like symptoms undergoing brain NCCT, CTA and CTP at a single centre. Patients with a final diagnosis of CVT (8) were analyzed together with a control group (40, 5:1 ratio) by three neurologists blinded to diagnosis. Brain NCCT+/-CTA showed poor sensitivity (37.5%) with high specificity (100%) for CVT diagnosis, which increased to 50% and 100% respectively after additional review of all the CTP maps. The discrimination of brain NCCT+/-CTA for CVT was moderate, AUC of 68.8 (95% CI: 50.8-86.7), increasing to AUC of 75 (95% CI: 56.5-93.5) after adding all the CTP maps reviews.

Background and purpose: Recent studies have demonstrated bias in various medical imaging artificial intelligence (AI) models, yet the factors underpinning these biases remain relatively unclear. This study evaluated potential sociodemographic biases in AI-based glioblastoma MRI segmentation models trained on datasets varying in size and demographic composition. We evaluated four nnUNet models with different training datasets: (1) the Federated Tumor Segmentation postoperative (FeTS2) model trained on a large (>10k exams) multi-national, multi-institution dataset, (2) the Brain Tumor Segmentation (BraTS) 2024 postoperative glioma model trained on a moderate size (>2k exams) multi-institution, North American dataset, (3) a model trained on a small (>200 exams), private, demographically homogenous, single-institution dataset, and (4) a model trained on an equally small (>200 exams), but demographically heterogenous dataset.

Materials and methods: Models were evaluated for bias using an independent, manually corrected dataset of 480 patients (mean age 52 ± 14) that was prospectively collected from a single high-volume academic brain tumor center. Automated FLAIR and enhancing tumor segmentations from the AI models were evaluated using Dice scores. Sociodemographic factors were collected and analyzed using beta regression to assess their influence on model performance.

Results: The model trained exclusively on White, non-Hispanic males had the lowest overall Dice scores (0.943 for FLAIR, 0.909 for Enhancement) and exhibited biases in age and smoking status. The BraTS model demonstrated the highest Dice scores (0.996 for FLAIR, 0.999 for Enhancement) and had the least bias overall.

Conclusions: Demographic bias was relatively low in glioblastoma MRI segmentation models. The model trained on the smallest and most homogenous dataset exhibited the most bias. Greater demographic heterogeneity even without increasing training dataset size was associated with reduced bias. The BraTS model, trained on a moderate-sized cohort that included more diverse tumor types, performed better and demonstrated less bias than the FeTS2 model, despite the FeTS2 being trained on the largest dataset.