Insomnia, the most common sleep disorder, is mainly defined by deficits in daytime functioning and challenges in keeping and starting sleep. The investigation of the white matter (WM) networks of the brain may yield respected insights into the fundamental neural approach of insomnia. The analysis lengthwise the perivascular space (ALPS) index and diffusion tensor imaging (DTI) have lately been presented as non-harmful instruments to evaluate these changes. Consequently, the purpose of this study was to judge WM microstructural modifications and glymphatic system function in adults with insomnia utilizing DTI-derived parameters and the ALPS index. This prospective case–control study included 50 adults analyzed with insomnia and fifty gender and age-matched healthy controls. All participants endured brain MRI with DTI acquisition. Quantitative analysis of mean diffusivity (MD), radial diffusivity (RD), fractional anisotropy (FA), and axial diffusivity (AD) was achieved in different WM areas, involving the corona radiata, internal capsule, thalamic radiation, superior longitudinal fasciculus, and corpus callosum. Glymphatic function was measured utilizing the DTI-ALPS index. Adults with insomnia demonstrated significantly decreased FA and improved RD and MD principles across many WM tracts, involving the anterior limb of the internal capsule, superior longitudinal fasciculus, superior corona radiata, and corpus callosum. Dyy, Dzz, and Dxx diffusivities were significantly increased, whereas the DTI-ALPS index was significantly reduced, representing glymphatic dysfunction. ROC analysis demonstrated excellent diagnostic performance of MD and RD parameters, particularly within the SLF and SCR. These results recommend that insomnia is related to both impaired glymphatic function and WM microstructural abnormalities. Insomnia is related to WM microstructural abnormalities and impaired glymphatic system function. The combination of DTI metrics and the ALPS index provides valuable, non-invasive biomarkers for early detection and characterization of neural alterations in insomnia.
Ankylosing spondylitis (AS) is a form of autoimmune spondyloarthritis that primarily affects the axial skeleton, including the spinal and sacroiliac joints. Magnetic resonance imaging (MRI), including assessment using the Spondyloarthritis Research Consortium of Canada (SPARCC) score and the identification of Romanus lesions, can provide information regarding imaging findings in AS. However, their association with clinical disease activity indices such as BASDAI, ASDAS-CRP, and ASDAS-ESR has been scarcely investigated in Indonesia. This study aimed to analyze the association between MRI sacroiliitis severity based on the SPARCC score and Romanus lesions with BASDAI, ASDAS-CRP, and ASDAS-ESR scores in patients with ankylosing spondylitis. This observational analytic study with a cross-sectional design included 48 patients with ankylosing spondylitis who underwent non-contrast MRI of the sacroiliac joints and spine and met the study criteria. Sacroiliitis severity was assessed using the SPARCC score, while Romanus lesions were assessed based on their presence or absence on MRI. Their associations with BASDAI, ASDAS-CRP, and ASDAS-ESR scores were analyzed using Spearman’s rank correlation and the Mann–Whitney U-test, as appropriate. The SPARCC score showed a strong positive correlation with BASDAI (rho = 0.856; p < 0.001) and moderate positive correlations with ASDAS-CRP (rho = 0.622; p < 0.001) and ASDAS-ESR (rho = 0.662; p < 0.001). No significant differences in BASDAI, ASDAS-CRP, or ASDAS-ESR scores were observed between subjects with and without Romanus lesions (p = 0.193, p = 0.081, and p = 0.228, respectively). In this cross-sectional study, higher SPARCC scores were associated with higher clinical disease activity scores. However, the cross-sectional design does not allow conclusions regarding the temporal relationship or causality between MRI findings and clinical disease activity. Romanus lesions were not significantly associated with BASDAI, ASDAS-CRP, or ASDAS-ESR scores.
Venous infarctions can present with varied imaging appearances and, in certain cases, may mimic intracranial tumours or space-occupying lesions. This resemblance can lead to diagnostic confusion and potentially unnecessary invasive procedures. The purpose of this study was to describe the neuroimaging features of venous infarction presenting as tumour-like cortical lesions and to characterize a recurrent sulcal imaging pattern, termed the sulcal tram-track appearance, as a potential diagnostic clue. A retrospective review was conducted of patients with venous infarction presenting with imaging features that mimicked a space-occupying lesion or demonstrated a tumour-like appearance. Imaging features were analysed using conventional MRI sequences, diffusion-weighted imaging (DWI), and susceptibility-weighted imaging (SWI). Particular attention was paid to lesion morphology, relationship to cortical sulci, diffusion characteristics, susceptibility findings, and enhancement patterns. Eleven patients were identified with venous infarction, where the lesions were predominantly cortically based and mimicked space-occupying lesions. T2-weighted/FLAIR imaging demonstrated paired linear hyperintensities along opposing banks of the affected sulcus in all 11 cases (100
Post-COVID-19 condition (Long COVID) has emerged as a major public health challenge, with survivors frequently reporting persistent dyspnea, fatigue, and exercise intolerance. While initial radiological research focused on parenchymal lung disease, a paradigm shift has identified pulmonary vascular abnormalities—specifically endotheliitis, microthrombosis, and maladaptive angiogenesis—as critical drivers of the Long COVID phenotype. The disease is characterized by “endotheliitis,” where direct viral entry via ACE2 receptors precipitates a pro-coagulant state and the formation of in situ microthrombi. This is further complicated by immunothrombosis involving neutrophil extracellular traps (NETs) and the presence of amyloid-like fibrin microclots that are resistant to natural breakdown. Additionally, structural remodeling through intussusceptive angiogenesis and “vascular pruning” (the loss of distal capillaries) reduces the effective surface area for gas exchange. Conventional CT pulmonary angiography (CTPA) often fails to detect these microvascular changes, despite patients exhibiting severe symptoms. However, advanced modalities provide deeper insights. Dual-energy CT (DECT) reveals persistent, wedge-shaped perfusion defects even in morphologically normal lung parenchyma. Quantitative CT (QCT) allows for the measurement of “vascular pruning,” showing a significant reduction in small vessel volume (PBV5). Hyperpolarized xenon-129 MRI (Hp-XeMRI) has identified a hallmark Red blood cell (RBC):barrier defect, indicating impaired gas transfer from the alveoli to red blood cells—a finding that persists even when standard CT and pulmonary function tests are normal. Functional techniques like V/Q SPECT and phase-resolved functional lung (PREFUL) MRI further confirm a “mottled” perfusion pattern indicative of diffuse small vessel vasculopathy. This review emphasizes that Long COVID involves a distinct vascular phenotype that requires functional and quantitative imaging for accurate diagnosis. Adherence to the 2025 international consensus guidelines—recommending imaging 3 months post-infection and the use of paired inspiratory/expiratory protocols—is essential for standardizing care and differentiating vascular from airway-related symptoms.
Detecting pulmonary infiltrates on pediatric chest radiographs remains challenging because of age-related anatomical variability and subtle radiographic findings. Artificial intelligence (AI) may assist radiologists in improving diagnostic efficiency and image interpretation. To evaluate the performance of a MobileNet V2-based deep learning model in detecting pulmonary infiltrates on pediatric chest radiographs and assess its agreement with radiologist consensus. A total of 600 pediatric chest radiographs (300 normal and 300 infiltrate images) were included. The dataset was divided into training (65
