Purpose: To investigate relationships among relaxation, susceptibility, and diffusion parameters in healthy white matter (WM) and to characterize WM MRI feature organization using an integrative multimodal quantitative MRI framework.

Methods: Twenty-two healthy volunteers underwent 3T MRI. Quantitative parameter mapping provided R₁, R₂*, R₁·R₂*, and quantitative susceptibility mapping, while diffusion kurtosis imaging provided fractional anisotropy (FA), mean kurtosis, axial kurtosis (AK), and radial kurtosis. All maps were spatially normalized to Montreal Neurological Institute (MNI) space, and mean values were extracted from WM tracts defined by the Johns Hopkins University (JHU) White Matter Atlas. Pearson correlation analysis with false discovery rate correction, principal component analysis (PCA), hierarchical clustering, and bootstrap stability analysis were performed.

Results: The first 2 principal components explained 77.7% of the total variance. The first principal component (PC1) was mainly associated with relaxation-related parameters and diffusion kurtosis metrics, whereas the second principal component (PC2) was characterized by opposing contributions from AK and FA. Quantitative susceptibility mapping showed weak correlations with other parameters (r = −0.37 to −0.08), suggesting relatively independent susceptibility-related information. The PCA structure was preserved after excluding R₁·R₂*, and bootstrap analysis supported loading stability, with mean loading correlations of 0.94 for PC1 and 0.90 for PC2. WM tracts formed 4 major cluster-like groups and showed tract-specific multimodal fingerprints.

Conclusion: Multimodal quantitative MRI provides a concise tract-level MRI feature representation of WM by integrating relaxation, susceptibility, and diffusion information. This exploratory framework may support future studies investigating subtle WM alterations, although validation in pathological cohorts is required.

Purpose: Assessing brain stiffness alterations associated with normal aging, sex-related differences, and regional variations is important from clinical perspective. The study aimed to investigate the changes in brain stiffness according to age and sex using virtual magnetic resonance elastography (vMRE), a noninvasive and novel technique based on diffusion-weighted imaging (DWI).

Methods: We retrospectively reviewed 96 adults (31–80   years, 51 men and 45 women) who underwent MRI as part of routine medical examinations. The shifted apparent diffusion coefficient was calculated from DWI (b   =   200 and 1500   sec/mm²) and converted to the DWI-based virtual shear modulus (μ_diff). Brain stiffness was measured in 12 brain regions: the cerebrum, cerebral gray/white matter, basal ganglia, thalamus, frontal lobe, parietal lobe, temporal lobe, occipital lobe, cerebellum, hippocampus, and amygdala. Linear regression and multiple-comparison tests were performed to evaluate the effects of age and regional differences.

Results: Brain stiffness exhibited regional differences, with higher shear modulus values observed in deep structures (thalamus and basal ganglia) compared to that in lobar regions (P   <   0.01). Among lobar areas, the occipital lobe had the highest stiffness, whereas the temporal lobe had the lowest (P   <   0.01). Linear regression revealed a significant negative correlation between age and brain stiffness (−0.06%/year, R²   =   0.09, P   =   0.04). The most pronounced decline in elasticity (−0.28%/year, R²   =   0.66, P   <   0.01) was observed in cerebral gray matter. Hippocampal stiffness significantly decreased after the fifth decade of life. Sex-related differences were found only in the parietal lobe (P   =   0.02) and basal ganglia (P   =   0.03).

Conclusion: Our findings suggest that vMRE can effectively measure the elastic properties of the brain. Brain stiffness decreases with age, but these changes vary across different regions.

Purpose: To investigate the effects of free-water (FW) correction on mean diffusivity (MD) values in glioblastomas using diffusion tensor imaging (DTI) with different b-value settings. We also examined regional differences between contrast-enhanced (CE) tumor and peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintense regions.

Methods: DTI data from 787 participants with glioblastoma were obtained from 2 open-source datasets: the University of California, San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM; b-value, 2000) and the Multi-parametric Magnetic Resonance Imaging Scans for De Novo Glioblastoma Patients from the University of Pennsylvania Health System (UPENN-GBM; b-value, 1000). Conventional and FW-corrected MD maps were generated, and median MD values were calculated from CE and FLAIR hyperintense regions. The impact of FW correction was assessed via the Wilcoxon matched-pairs signed rank test, Bland–Altman plot, and Spearman’s correlation analysis. Multivariate Cox regression was used to evaluate prognostic significance, adjusting for age and CE volume.

Results: Both median MD values were significantly lower in the CE regions than in the FLAIR hyperintense regions. In the UCSF-PDGM dataset, FW correction caused a greater reduction of MD in the CE regions, whereas the greater reduction occurred in the FLAIR hyperintense regions in the UPENN-GBM dataset. Bland–Altman plot also indicated lesions with lower MD values showing greater FW correction in the UCSF-PDGM dataset, whereas those with relatively high MD values showed greater FW correction in the UPENN-GBM dataset. The correlation between both MD values was high (Rs > 0.98). Neither conventional MD nor FW-corrected MD values in the CE regions were significantly associated with overall survival. (Ps > 0.30).

Conclusion: A paradoxical relationship of MD values with and without FW correction was identified depending on regional characteristics and b-value settings, underscoring the importance of acquisition parameters when interpreting FW correction.

We report a case of Wallenberg syndrome associated with vertebral artery occlusion and suspected atherosclerotic changes. The patient was evaluated using accelerated 3D fluid-attenuated inversion recovery MRI incorporating T2 preparation and magnetization transfer contrast pulses. This technique enabled a shortened acquisition time while preserving high image contrast, allowing clear visualization of the lateral medullary infarction. This approach provides a non-contrast, high-resolution method for assessing brainstem infarcts and may contribute to improved diagnostic accuracy in the acute setting.

Purpose: The postmortem interval (PMI) alters tissue properties that shape quantitative MRI (qMRI) signals. We systematically investigated the effects of PMI on relaxation times, depending on tissue characteristics, in both unfixed and fixed pig brains.

Methods: Twelve pig brains (n = 12) were scanned in both unfixed and fixed states at 3 PMI windows (≈12, 24, 48h). Quantitative T1, T2, and T2* maps were acquired with identical protocols at controlled room temperature (~24°C). We assessed the PMI effects on relaxation times in the gray matter (GM) and the white matter (WM) at both group and sample levels, while fixation-induced effects and inter-sample variability were evaluated using pairwise rank correlation and coefficient of variation (CV) visualization.

Results: In unfixed tissues, T1 significantly differed among PMI groups in GM (P = 0.0141) and WM (P = 0.0315) in the early window (≤24h). In the same window, PMI–T1 correlations were observed in both GM (r = 0.921, P = 0.007) and WM (r = 0.876, P = 0.013). T2 showed no group differences but exhibited an inverse correlation with PMI in WM (r = −0.849, P = 0.015). No significant PMI–T2* relationships were detected (all P > 0.05). At later PMI (20–50h), PMI–qMRI correlations diminished. Fixation processes altered all qMRI parameters. Notably, the PMI effects on T1 in the unfixed brains were preserved even after fixation at an ordinal level, although fixation introduced substantial inter-sample variability.

Conclusion: PMI exerts the most robust effects on T1. Fixation has a significant impact on qMRI values, mitigating the apparent PMI effect and increasing the inter-sample variability. However, it is still possible to retrieve the PMI effects from fixed brain at the level of rank order, providing practical guidance for further ex vivo qMRI studies on PMI.

Purpose: Cranial nerve imaging with 3T MRI commonly uses 3D fast imaging employing steady-state acquisition (3D-FIESTA); however, this sequence has limitations in achieving higher spatial resolution and provides poor tissue contrast between cranial nerves and adjacent vascular structures. We evaluated 3D T2-weighted imaging (T2-CUBE) with deep learning–based reconstruction (DLR) for cranial nerve visualization, focusing on the trochlear nerve, the smallest cranial nerve with the longest intracranial course, and compared it with T2-CUBE without DLR and 3D-FIESTA.

Methods: Ten healthy male volunteers (age, 23–40 years; mean age, 32 years) underwent T2-CUBE with and without DLR, and 3D-FIESTA at 3T. Two neuroradiologists independently evaluated trochlear nerve visualization in 4 anatomical segments (origin from the midbrain, cisternal, tentorial, and anterior portion of its cavernous segments) using a 3-point scale, and SNR of the pons (SNR_PONS) and cerebrospinal fluid (SNR_CSF) were calculated.

Results: T2-CUBE with DLR achieved a 100% visualization across all trochlear nerve segments and demonstrated significantly better visualization than both T2-CUBE without DLR and 3D-FIESTA (P < 0.025). T2-CUBE without DLR showed 67.5%–100% visualization across the 4 segments. 3D-FIESTA showed 32.5%–80% visualization of the origin from the midbrain, cisternal, and tentorial segments, with no visualization of the cavernous segments. DLR increased SNR_PONS and SNR_CSF by factors of 1.8–2.5 (SNR_PONS: 14.1 vs 5.7; SNR_CSF: 31.8 vs 17.5, respectively; P < 0.001). T2-CUBE with DLR demonstrated significantly higher SNR_PONS than 3D-FIESTA (14.1 vs 6.4, P < 0.001), while SNR_CSF was comparable (31.8 vs 36.3, P = 0.20).

Conclusion: T2-CUBE with DLR at 3T provided a significantly better trochlear nerve visualization than T2-CUBE without DLR and 3D-FIESTA. This technique may extend beyond the trochlear nerve to other cranial nerves and to the evaluation of neurovascular compression in the cistern, with the potential to become the new standard for cisternal imaging.

Purpose: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder influenced by genetic factors, with a long preclinical phase characterized by subtle alterations in brain microstructure. Although the apolipoprotein E ε4 allele is a well-established genetic risk factor, AD is increasingly recognized as a polygenic condition. However, how polygenic risk manifests in white matter (WM) microstructure and cognition remains unclear. This study aims to investigate the associations between AD polygenic risk scores (ADPRS), WM microstructure, and cognitive performance in clinically asymptomatic adults.

Methods: Data from 36400 individuals (aged 45–83 years) in the UK Biobank were analyzed. Diffusion tensor imaging and neurite orientation dispersion and density imaging metrics were extracted from 48 WM tracts. General linear models were used to examine associations between ADPRS, WM integrity, and 10 cognitive measures. Mediation analyses were conducted to test whether WM microstructure mediated the relationship between ADPRS and cognitive performance.

Results: Higher ADPRS was significantly associated with reduced fractional anisotropy and intracellular volume fraction, and with increased mean, axial, and radial diffusivity, as well as isotropic volume fraction across limbic and association fibers, particularly in the cingulum hippocampus, fornix, posterior thalamic radiation, and superior fronto-occipital fasciculus. Higher ADPRS was also associated with poorer cognitive performance, most prominently on tests of executive function (Trail Making Test B), episodic memory (Paired Associate Learning), and processing speed (Symbol Digit Substitution). Mediation analysis revealed that WM microstructural alterations, especially increased radial diffusivity and reduced fractional anisotropy in the posterior thalamic radiation, partially mediated the association between ADPRS and cognitive performance, accounting for up to 4.88% of the total effect.

Conclusion: ADPRS is linked to selective WM microstructural alterations and subtle cognitive difference in clinically asymptomatic adults. WM microstructural changes partially mediate the association between ADPRS and cognitive performance.

Purpose: Glioblastoma (GBM) is an aggressive brain tumor with poor prognosis. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation is a critical biomarker for guiding chemotherapy decisions, yet current testing requires invasive tissue sampling. This study aimed to systematically evaluate the diagnostic accuracy of artificial intelligence (AI) models using MRI for non-invasive prediction of MGMT promoter methylation status in GBM.

Methods: We conducted a systematic search of PubMed, ScienceDirect, Scopus, Google Scholar, Cochrane, Web of Science and EMBASE, identifying 480 records. After duplicate removal and screening, 14 studies met inclusion criteria. Data extracted included AI model architecture, MRI sequences, segmentation methods, and diagnostic metrics. A bivariate random-effects model was used to pool sensitivity and specificity. Meta-regression analyses assessed the effect of AI model type on diagnostic performance. Study quality was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool.

Results: The bivariate random-effects model yielded a pooled sensitivity of 0.536 (95% confidence interval [95% CI]: 0.509–0.563) and a pooled specificity of 0.514 (95% CI: 0.454–0.574), indicating moderate between-study heterogeneity, with an area under the curve of 0.56. The best-performing models included MGMT-net and transformer-based architectures, particularly when using multimodal MRI inputs. Studies employing automated segmentation and single-sequence input (e.g., T2-weighted only) generally demonstrated lower performance. QUADAS-2 assessment indicated a low risk of bias in most domains, with concerns regarding index test thresholds and external validation in some studies.

Conclusion: AI-based MRI models show moderate-to-high potential for non-invasive MGMT methylation prediction in GBM. However, heterogeneity in study design, imaging protocols, and validation approaches highlights the need for standardized methodologies and robust external validation before clinical adoption.

Purpose: This study evaluated the performance of artificial intelligence (AI)-based brain aneurysm detection software in clinical settings, aiming to assess its utility as a supportive tool for radiologists. Metrics included sensitivity, positive predictive value (PPV), F1 score, and false positives (FPs) per case.

Methods: A retrospective analysis of 442 cases (March 2023–August 2024) compared AI detections against a reference standard derived from the radiologists’ assessments and image re-review. Findings were categorized into true positives (TPs), FPs, and false negatives (FNs). Subgroup analyses covered aneurysm size, magnetic field strength of the MRI, patient age, and aneurysm location.

Results: The study included 442 cases (226 males, 216 females; median age 72). Out of 94 total aneurysms, the AI detected 73 TP and missed 21 FN. It also identified 520 FP. Overall, sensitivity was 77.7%, PPV was 12.3%, and the F1 score was 0.212. The FPs averaged 1.18 per case. Sensitivity varied by aneurysm size: 85.1% for ≤ 3 mm, 69.2% for 3–5 mm, and 50.0% for > 5 mm. Significant variability in FPs per case was observed across different magnetic field strengths. Performance also varied by patient age and aneurysm location.

Conclusion: The AI software demonstrated moderate sensitivity, especially for smaller aneurysms. Variations in performance across different magnetic field strengths and aneurysm size suggest a need for more robust AI algorithms. Detailed analysis of aneurysm locations provides insights into areas where AI performance could be enhanced. Integrating the AI software as a supportive tool, combined with radiologist expertise, is hypothesized to enhance detection accuracy, though further studies are needed to quantify this combined effect.

Purpose: This study aimed to evaluate the diagnostic utility of locus coeruleus (LC) signal intensity on high-resolution T1-weighted imaging with magnetization transfer (T1WI with MT) for distinguishing Parkinson’s disease (PD) from a broad range of atypical parkinsonism (AP) subtypes, including early-stage cases.

Methods: We retrospectively analyzed T1WI with MT data from 214 participants, including patients with PD (n = 125), corticobasal syndrome (CBS, n = 12), multiple system atrophy (MSA, n = 16), progressive supranuclear palsy (PSP, n = 19), essential tremor (n = 17), vascular parkinsonism (n = 4), drug-induced parkinsonism (DIP, n = 7), and healthy subjects (HS, n = 14). Circular ROIs were placed on the LC and substantia nigra pars compacta to calculate contrast ratios (CRs). Conventional MRI findings of AP, focusing on characteristic regional atrophy patterns, were also evaluated. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis. A subanalysis was performed for early-stage cases (within 2 years of onset). Three independent neuroradiologists evaluated T1WI with MT, and interobserver agreement was assessed using intraclass correlation coefficients (ICC).

Results: The LC-CR was significantly lower in PD than in HS (P < 0.01) and all AP subtypes except DIP (P = 0.37). ROC analysis revealed that LC-CR had the highest diagnostic accuracy for differentiating PD from AP (area under the curve [AUC] = 0.83, sensitivity = 67%, specificity = 90%). In early-stage cases, LC-CR maintained high specificity (98%) with an AUC of 0.80. The diagnostic utility of LC-CR was comparable or superior to conventional MRI findings in distinguishing PD from CBS, MSA, and PSP. Interobserver agreement for LC-CR measurements was good, with an ICC of 0.87 (95% confidence interval: 0.85–0.89).

Conclusion: LC-CR measured on T1WI with MT serves as a reliable imaging biomarker for differentiating PD from various forms of AP, even in early disease stages.

Purpose: To develop and evaluate short-TR acquisition time-of-flight (STRA-TOF) MR angiography (MRA), which combines an optimized STRA with deep learning–based reconstruction to achieve scan-time reduction while maintaining image quality in the visualization of intracranial arteries.

Methods: Ten healthy volunteers and 3 patients with moyamoya disease were examined using 3D TOF MRA with the clinical moyamoya protocol and 2 STRA-TOF protocols employing 4-slab (STRA4) and 9-slab (STRA9) configurations. STRA-TOF employed a TR of approximately 10 ms with variable-density Poisson-disc sampling and unrolled deep learning reconstruction. Bloch equation simulations validated the theoretical basis for STRA. Quantitative assessment included SNR and contrast-to-noise ratio measurements. Two radiologists independently evaluated image quality using a 3-point scale across 9 vascular territories and overall image quality, with blinded assessment. Statistical analysis was performed using the Friedman test with post hoc Wilcoxon signed-rank tests.

Results: STRA-TOF achieved approximately a 50% reduction in scan time compared with conventional protocols. Both STRA sequences demonstrated significantly higher SNR and contrast-to-noise ratio than conventional TOF (P < 0.001). Overall image quality scores were higher for STRA4 and STRA9 compared with conventional TOF in both readers. Across the 9 vascular territories, both readers consistently rated STRA sequences equal to or superior to conventional TOF, particularly for distal branches. In the small patient cohort with moyamoya disease (n = 3), STRA-TOF demonstrated feasibility for visualizing complex arterial pathology, including stenotic vessels, collateral circulation, and postsurgical vascular changes.

Conclusion: STRA-TOF achieved approximately a 50% reduction in scan time while maintaining or improving image quality compared with conventional 3D TOF MRA. This technique addresses the fundamental scan-time limitations of the conventional method, with potential for significant clinical benefits in terms of patient comfort, workflow efficiency, and improved access to intracranial artery evaluation.

This study evaluated the utility of deep learning reconstruction (DLR) in vessel wall imaging (VWI) for visualizing the entire cerebral arterial system, including cortical arteries. Seventeen patients underwent post-contrast 3D T1WI-CUBE VWI with 0.5 mm isotropic resolution. Images with and without DLR were compared using qualitative and quantitative assessments. Qualitative image quality was rated on a 4-point scale across 29 arterial segments, including the internal carotid, vertebral, basilar, and the 1st to 4th segments of the major cerebral arteries. Quantitative evaluation of the vertebral artery wall assessed SNR and contrast-to-noise ratio (CNR). DLR significantly improved overall image quality compared to the without-DLR group, with cortical arteries rated as optimal in all cases with DLR (all P < 0.001). SNR and CNR were also significantly higher with DLR (P = 0.004). These results suggest that DLR enables high-resolution VWI of intracranial cortical arteries within a clinically acceptable scan time.

We have developed a practical method to determine the optimal number of signal averages (NSAs) for acquiring a spectrum in the human brain and determined the optimal number of NSA at 7-Tesla. We performed 50 sequential data acquisitions with an NSA of 2 in healthy volunteers and then generated spectra with NSAs ranging from 2 to 100. After identifying the metabolites with Cramér–Rao lower bounds (CRLB) ≤ 15% in the NSA 100 spectrum, we examined the CRLB values, creatine+phosphocreatine (Cr + PCr) ratios and agreement of Cr + PCr ratios. Eight metabolites and 5 metabolite pairs spectrum showed CRLB values ≤ 15% when the NSA was 40 or higher. Additionally, the Cr + PCr ratios at NSA 40 closely matched those at NSA 100. By analyzing the CRLB values of metabolites in varying NSAs spectra generated from sequential data, we determined the optimal NSA needed to accurately measure the spectrum within a reasonable acquisition time.

This case report shows that paramagnetic rim lesions (PRLs), markers of chronic active lesions in multiple sclerosis, vary in visibility depending on scan-parameters of susceptibility-weighted imaging (SWI). Routine SWI for microbleed detection with low flip angle (FA) failed to depict PRLs, while longer TE and higher FA improved visibility. Phase images consistently visualized PRLs. These findings underscore the need to optimize TE and FA, as suboptimal SWI settings may hinder PRL detection.

Purpose: This study aimed to evaluate the diagnostic potential of macromolecular proton fraction (MPF) mapping combined with magnetic susceptibility measurements as subcortical biomarkers in Parkinson’s disease (PD).

Methods: Twenty patients with PD and 9 age-matched healthy controls underwent 3.0T magnetic resonance imaging, including MPF mapping and quantitative susceptibility mapping (QSM). MPF and magnetic susceptibility values of the 16 subcortical nuclei were measured in both the PD group and healthy control group, and these values were compared between the 2 groups. Diagnostic performance of MPF and magnetic susceptibility in the subcortical nuclei was individually evaluated using receiver operating characteristic (ROC) analysis. A logistic regression model was developed for the diagnosis of PD using a combination of MPF and magnetic susceptibility values.

Results: Quantitative analyses revealed significantly reduced MPF values (false discovery rate–corrected P < 0.05) in 7 subcortical nuclei in the PD group, namely the hypothalamus, parabrachial pigmented nucleus, red nucleus, substantia nigra pars reticulata, substantia nigra pars compacta, ventral pallidum, and ventral tegmental area. Additionally, magnetic susceptibility was significantly elevated (false discovery rate–corrected P < 0.05) in the parabrachial pigmented nucleus and substantia nigra pars compacta. ROC curve analysis demonstrated strong diagnostic performance, with the ventral pallidum showing the highest MPF-based diagnostic accuracy (area under the curve [AUC] = 0.82) and the substantia nigra pars compacta showing the highest QSM-based accuracy (AUC = 0.88). The logistic regression model combining MPF and magnetic susceptibility showed the best performance (AUC = 0.93).

Conclusion: MPF mapping, particularly when combined with magnetic susceptibility measurements, may serve as a quantitative diagnostic biomarker for PD, and the observed widespread alterations across multiple subcortical nuclei provide new insights into the pathology of PD beyond the classic nigrostriatal pathway.

Purpose: Glymphatic system impairment has been suggested in previous animal and human studies regarding traumatic brain injury (TBI). Diffuse axonal injury (DAI) is an important pathological feature of TBI and is frequently diagnosed in patients with moderate to severe TBI. This study evaluated the glymphatic system function in patients with DAI using diffusion tensor imaging analysis along the perivascular space (DTI-ALPS), a non-invasive technique.

Methods: A total of 162 patients with TBI, including 84 with DAI and 78 without DAI, underwent MRI with DTI within 6 months of the date of injury. The ALPS index was calculated to assess the glymphatic system activity and compared between patients with and without DAI. Analysis of covariance (ANCOVA) was used to compare the ALPS index between patients with DAI grades 1, 2, and 3. Correlation analysis was performed between the ALPS index, DAI grade, and Glasgow Coma Scale (GCS) score.

Results: Patients with DAI (1.29 ± 0.17) had a significantly lower ALPS index than those without DAI (1.42 ± 0.19, P < 0.001). The ALPS index differed significantly between patients with different DAI grades (ANCOVA, P < 0.001). The ALPS index and DAI grades were negatively correlated (r =−0.47, P < 0.001). The ALPS index and GCS scores showed a weak positive correlation (r = 0.174, P = 0.027).

Conclusions: Patients with DAI have a lower ALPS index, indicating impaired glymphatic system activity, which is more severe in patients with a higher DAI grade. These findings broaden the understanding of the pathophysiology of DAI and help predict patients’ prognoses and recovery trends.

Stroke is a major cause of disability and death in adults worldwide. In clinical setting, time efficient MRI protocols including diffusion weighted images, fluid attenuated inversion recovery, fast T2*-weighted images and MR angiography aim to establish ischemic stroke diagnosis, visualize vessel occlusion and determine the extent of ischemia damage distinguishing between the salvageable ischemic penumbra and the infarct core. Thus, MRI plays a pivotal role in diagnosis, treatment decision-making, and prognostic assessment, although prediction based on these elements remains limited and can be improved. We reviewed the added-values of alternative MRI methods such as sodium(²³Na) MRI and ¹H MR spectroscopic imaging that open new temporal and pathophysiological windows on ionic dys-homeostasis and metabolism alteration in the context of ischemic stroke and reperfusion. Insights on the timeline of the cellular events accessible using these alternative methods and perspectives to improve clinical outcome prediction of ischemic stroke patients are discussed.

Purpose: This study aims to assess thalamocortical tract fiber injury using diffusion-tensor imaging (DTI) and to characterize metabolic alterations in the dorsal thalamus with proton magnetic resonance spectroscopy (MRS) in patients with cervical spondylotic myelopathy (CSM).

Methods: A prospective study involved 98 CSM patients and 66 age-matched controls without neurological disease, recruited from May 2021 to December 2021. Neurological function was evaluated using the modified Japanese Orthopaedic Association (mJOA) score. DTI and MRS were analyzed by 2 experienced radiologists, with statistical analysis performed via SPSS (v.26), including t-tests, chi-square tests, and Pearson’s correlation.

Results: DTI revealed significantly lower fractional anisotropy (FA) and higher radial and axial diffusivity in the bilateral postcentral gyrus of CSM patients compared to controls (P < 0.001). MRS showed decreased ratios of N-acetylaspartate (NAA)/creatinine (Cr), choline (Cho)/Cr, and myo-inositol (mI)/Cr in the dorsal thalamus of CSM patients (P < 0.001). Correlation analysis indicated a moderate association between mJOA scores and NAA/Cr and mI/Cr ratios, and a weaker correlation with FA and Cho/Cr.

Conclusion: CSM patients exhibit significant thalamocortical disruptions and metabolic changes in the dorsal thalamus, correlating with clinical severity. These findings suggest that DTI and MRS could provide valuable insights into the extent of neural damage in CSM.

Purpose: The purposes of this study were 1) to improve vessel visibility of our MR sequence by modifying k-space filling and 2) to verify the usefulness of applying artificial intelligence (AI) for volume isotropic simultaneous interleaved bright- and black-blood examination (VISIBLE) with compressed sensitivity encoding (CS) in autodetecting brain metastases.

Methods: We modified 3 sequences of VISIBLE (Centric, Reversed Centric, and Startup Echo 30). The Centric sequence is a prototype. The Reversed Centric filled the k-space in a reversed centric manner to improve vessel visibility. The Startup Echo 30 implemented dummy echoes to further improve vessel visibility. Vessel visibility was evaluated in one slice at the level of the centrum semiovale. The sensitivity, specificity, the area under the curve (AUC), and false positives of detecting brain metastases using AI were evaluated among 3 sequences. Statistical comparisons were performed using a one-way analysis of variance, followed by Friedman and Dunn’s multiple comparison tests.

Results: The number of visualized vessels was significantly lower in the Centric (39.3 ± 9.7, P < 0.05) and Reversed Centric (44.2 ± 9.8, P < 0.05) methods than in the magnetization-prepared rapid gradient echo (49.3 ± 9.1) but comparable in the Startup Echo 30 method (44.9 ± 8.8, P > 0.05). No significant differences existed in sensitivity, specificity, and AUC among the 3 methods. False positives achieved using the Reversed Centric method were significantly fewer (54 false positives) than those achieved using the Centric (85 false positives) and Startup Echo 30 (68 false positives) methods (P = 0.0092).

Conclusion: Vessel visibility was improved by modifying the k-space filling, which may reduce false positives. The AI model for VISIBLE with CS achieved good performance in autodetection of brain metastases. The AI model for VISIBLE with CS can help radiologists diagnose brain metastases in clinical practice.