The glymphatic system is a newly discovered waste-clearing system that is analogous to the lymphatic system in our central nervous system. Furthermore, disruption in the glymphatic system has also been associated with many neurodegenerative disorders (e.g., Alzheimer’s disease), traumatic brain injury, and subarachnoid hemorrhage. Thus, understanding the function and structure of this system can play a key role in researching the progression and prognoses of these diseases. In this review article, we discuss the current ways to map the glymphatic system and address the advances being made in preclinical mapping. As mentioned, the concept of the glymphatic system is relatively new, and thus, more research needs to be conducted in order to therapeutically intervene via this system.

This imaging essay reviews the typical and atypical imaging findings of sarcoidosis at chest radiography, CT, PET/CT, and MRI and discusses common differential diagnoses.

Sarcoidosis is a systemic illness with unclear etiology that commonly affects the lungs and intrathoracic lymph nodes. Chest radiography, CT, and, more recently, MRI can be used for assessment, with bilateral hilar lymph node enlargement and interstitial lung disease being common observations. In adults diagnosed with pulmonary sarcoidosis, how do chest radiography, CT, and MRI compare in the assessment of disease progression? This is a prospective study of 77 adults diagnosed with pulmonary sarcoidosis. Each patient underwent pulmonary function testing, chest radiography, HRCT and MRI within a span of 90 days. Chest radiographs were scored using the Scadding stage classification system. HRCT and MRI were both scored using the Scleroderma Lung Study I system. Pulmonary function was re-assessed after 12 months, with progression of disease defined as worsening symptoms AND ≥5% reduction of forced vital capacity (FVC). Disease progression was used as the gold standard to calculate area under the curve (AUC) of the receiver operating characteristic plot for radiography, HRCT, and MRI. There is strong correlation between chest radiography and MRI (r=0.649, P < 0.001), and CT and MRI scores (r=0.851, P < 0.05). CT and MRI scores correlated with forced vital capacity (MRI: r =-0.584, P < 0.001; CT: r =-0.308, P = 0.049) and diffusing capacity of the lung for carbon monoxide (MRI: r =-0.564, P =0.004; CT r =-0.216, P = 0.017). AUCs for chest radiography, MRI and CT scores were 0.70 (0.49-0.85), 0.71 (0.42-0.85), and 0.68 (0.26-0.90), respectively. Multivariate regression analysis of CT and MRI scores demonstrated statistically significant prediction of progressive disease by both modalities. MRI may be a viable alternative to HRCT in the assessment of lung parenchyma and disease progression in patients with pulmonary sarcoidosis.

Objective: This study aims to assess the accuracy of pulmonary nodule detection via MRI compared to the gold standard, CT, in patients with extrathoracic cancer. Materials and Methods: MRI of the chest was performed on oncological patients for staging extrathoracic cancer and subsequently compared to their CT. Only the largest nodule was considered in patients with multiple nodules. Nodule size and malignancy were recorded for each modality, as well as the presence of interstitial lung disease (ILD), adenopathy, cardiomegaly, pleural effusion, pericardial effusion, and vertebral fracture. All cases were read by two thoracic radiologists and any discrepancies were resolved by discussion. Results: A total of 154 patients with nodules were identified from 241 participants (mean age: 59 years [18–95]). Average nodule diameter was 11.5 mm (range: 3.9–29.1 mm; SD: 8.1 mm). MRI detected all nodules greater than 5 mm. Malignancy was detected in 37 nodules. The sensitivity, specificity, and accuracy values of MRI for all nodules were 93.51%, 100%, and 95.85%, respectively. For ground-glass nodules (n = 11), the values were 43.6%, 100%, and 65.0%, respectively. When compared to CT, long-axis diameter measured by MRI was underestimated by 9 ± 2.3% (p < 0.001). There was a strong correlation between measurements of CT and MRI (κ = 0.70–1.00). Furthermore, MRI accurately detected the presence of adenopathy (97.1%), cardiomegaly (99.17%), pleural effusion (98.34%), pericardial effusion (100%), and vertebral fracture (99.6%). Conclusions: These findings suggest that lung MRI accurately detects pulmonary nodules and other thoracic pathologies commonly observed in oncological patients. Lung MRI may serve as a substitute to CT for oncological patients undergoing routine extrathoracic surveillance, thereby decreasing radiation exposure.