Background and Purpose Obstructive sleep apnea (OSA) subjects show brain injury in sites that control autonomic, cognitive, and mood functions that are deficient in the condition. The processes contributing to injury may include altered blood-brain barrier (BBB) actions. Our aim was to examine BBB function, based on diffusion-weighted pseudo-continuous arterial spin labeling (DW-pCASL) procedures, in OSA compared to controls. Methods We performed DW-pCASL imaging in 9 OSA and 9 controls on a 3.0-Tesla MRI scanner. Global mean gray and white matter arterial transient time (ATT, an index of large artery integrity), water exchange rate across the BBB (Kw, BBB function), DW-pCASL ratio, and cerebral blood flow (CBF) values were compared between OSA and control subjects. Results Global mean gray and white matter ATT (OSA vs controls; gray matter, 1.691±0.120 vs 1.658±0.109 sec, p=0.49; white matter, 1.700±0.115 vs 1.650±0.114 sec, p=0.44), and CBF values (gray matter, 57.4±15.8 vs 58.2±10.7 ml/100g/min, p=0.67; white matter, 24.2±7.0 vs 24.6±6.7 ml/100g/min, p=0.91) did not differ significantly, but global gray and white matter Kw (gray matter, 158.0±28.9 vs 220.8±40.6 min−1, p=0.002; white matter, 177.5±57.2 vs 261.1±51.0 min−1, p=0.006), and DW-pCASL ratio (gray matter, 0.727±0.076 vs 0.823±0.069, p=0.011; white matter, 0.722±0.144 vs 0.888±0.100, p=0.004) values were significantly reduced in OSA over controls. Conclusions OSA subjects show compromised BBB function, but intact large artery integrity. The BBB alterations may introduce neural damage contributing to abnormal functions in OSA, and suggest a need to repair BBB function with strategies commonly used in other fields.
The automated classification of brain tumors plays an important role in supporting radiologists in decision making. Recently, vision transformer (ViT)-based deep neural network architectures have gained attention in the computer vision research domain owing to the tremendous success of transformer models in natural language processing. Hence, in this study, the ability of an ensemble of standard ViT models for the diagnosis of brain tumors from T1-weighted (T1w) magnetic resonance imaging (MRI) is investigated. Pretrained and finetuned ViT models (B/16, B/32, L/16, and L/32) on ImageNet were adopted for the classification task. A brain tumor dataset from figshare, consisting of 3064 T1w contrast-enhanced (CE) MRI slices with meningiomas, gliomas, and pituitary tumors, was used for the cross-validation and testing of the ensemble ViT model’s ability to perform a three-class classification task. The best individual model was L/32, with an overall test accuracy of 98.2% at 384 × 384 resolution. The ensemble of all four ViT models demonstrated an overall testing accuracy of 98.7% at the same resolution, outperforming individual model’s ability at both resolutions and their ensembling at 224 × 224 resolution. In conclusion, an ensemble of ViT models could be deployed for the computer-aided diagnosis of brain tumors based on T1w CE MRI, leading to radiologist relief.
Obstructive sleep apnea (OSA) is characterized by recurrent upper airway blockage, with continued diaphragmatic efforts to breathe during sleep. Brain structural changes in OSA appear in various regions, including white matter sites that mediate autonomic, mood, cognitive, and respiratory control. However, the relationships between brain white matter changes and disease severity in OSA are unclear. Our aim was to examine associations between an index of tissue integrity, magnetization transfer (MT) ratio values, which show MT between free and proton pools associated with tissue membranes and macromolecules, and disease severity [apnea-hypopnea index (AHI)] in OSA subjects. We collected whole-brain MT imaging data from 19 newly-diagnosed, treatment-naïve OSA subjects [age, 50.4±8.6 years; 13 males; AHI, 39.7±24.3 events/hour], using a 3.0-Tesla MRI scanner. Using these data, whole-brain MT ratio maps were calculated, normalized to common space, smoothed, and correlated with AHI scores using partial correlation analyses (covariates; age, gender; p<0.005). Multiple brain sites in OSA subjects, including superior and inferior frontal regions, ventral medial pre-frontal cortex and nearby white matter, mid-frontal white matter, insula, cingulate and cingulum bundle, internal and external capsules, caudate nuclei and putamen, basal forebrain, hypothalamus, carpus callosum, and temporal regions showed principally-lateralized negative correlations (p<0.005). These regions showed significant correlations even with correction for multiple comparisons (cluster-level, family wise error, p<0.05), except for a few superior frontal areas. Predominately negative correlations emerged between local MT values and OSA disease severity, indicating potential usefulness of MT imaging to examine the OSA condition. The findings indicate that OSA severity plays a significant role in white matter injury.
Global mean kurtosis values are significantly increased in obstructive sleep apnea (OSA), suggesting acute tissue injury, and these changes are principally localized in critical sites mediating deficient functions in the condition. The mechanisms for injury likely include altered perfusion and hypoxemia-induced processes, leading to acute tissue changes in recently diagnosed OSA.
We present methods to quantify the medial tibio- femoral (MTF) joint contact area (CA) and congruity index (CI) from low-field magnetic resonance imaging (MRI). Firstly, based on the segmented MTF cartilage compartments, we computed the contact area using the Euclidian distance transformation. The CA was defined as the area of the tibial superior surface and the femoral inferior surface that are less than a voxel width apart. Furthermore, the CI is computed point-by-point by assessing the first- and second-order general surface features over the contact area. Mathematically, it is the inverse distance between the local normal vectors (first-order features) scaled by the local normal curvatures (second-order features) along the local direction of principal knee motion in a local reference coordinate system formed by the directions of principal curvature and the surface normal vector. The abilities of the CA and the CI for diagnosing osteoarthritis (OA) at different levels (disease severity was assessed using the Kellgren and Lawrence Index, KL) were cross-validated on 288 knees at baseline. Longitudinal analysis was performed on 245 knees. The precision quantified on 31 scan-rescan pairs (RMS CV) for CA was 13.7% and for CI 7.5%. The CA increased with onset of the disease and then decreased with OA progression. The CI was highest in healthy and decreased with the onset of OA and further with disease progression. The CI showed an AUC of 0.69 (p < 0.0001) for separating KL = 0 and KL > 0. For separating KL < 1 or KL = 1 and KL > 1 knees, the AUC for CI was 0.73 (p < 0.0001). The CA demonstrated longitudinal responsiveness (SRM) at all stages of OA, whereas the CI did for advanced OA only. Eventually, the quantified CA and the CI might be suitable to help explaining OA onset, diagnosis of (early) OA, and measuring the efficacy of DMOADs in clinical trials.
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