Extensive spread of coronavirus disease (COVID-19) prompted to an active investigation of its diagnostic features. The case of acute viral pneumonia associated with COVID-19 has already been described in detail in CT and radiography, and MRI. There is practically no data in the literature on the descriptive picture observed with dynamic MRI. Considering comprehensive diagnostic approach, it is essential that radiologists know how to recognize and interpret COVID-19 on MRI correctly. This radiation-free tool applied to a patient with acute viral pneumonia caused by COVID-19 may prove extremely useful in cases when access to CT is limited and dynamic morphofunctional imaging is required.

Diagnosis of bone mineral disorders (MCI) is an urgent task in pediatrics. Of particular importance is the timely visualization of destructive changes in bone tissue, leading to significant deformations and impairment of the quality of life of patients. At the moment, there are no unified algorithms for radiological examination of children with chronic kidney disease (CKD), allowing early detection of disorders of mineral and bone metabolism. This clinical example demonstrates the difficulties of diagnosing a brown tumor in a child with primary type I hyperoxaluria. Along with this, the effectiveness of methods of radiation diagnostics in the protocol of dynamic observation of patients with the syndrome of mineral and bone disorders in chronic kidney disease (MCD-CKD) is presented.

Aim: Evaluate the ability of magnetic resonance imaging (MRI) of the chest to detect malignant pulmonary nodules compared to computed tomography (CT). Materials and Methods: We searched the following databases with the final date of search on April 7th, 2021: PubMed, Google Scholar. According to the inclusion and exclusion criteria, we selected studies that assessed the detection of malignant lung nodules by both MRI and CT and included information about sensitivity and specificity. Method of the analysis and data grouping was chosen with regard to statistical heterogeneity of the studies included in the analysis. We used 2 test and I2 statistic to evaluate the heterogeneity. Results: For the systematic review we selected 168 articles found in the PubMed and Google Scholar databases. 21 studies on 1188 patients were included in the meta-analysis. Meta-analysis revealed the presence of statistically significant heterogeneity (P 0,00001 for 2 test; I2 = 99%) for sensitivity and specificity. Hence, we used random effect model for further analysis. As result, values of sensitivity for detection of pulmonary nodules with MRI ranged from 70.4 to 100%, specificity - from 60.6 to 100%. Conclusions: MRI has sufficient sensitivity and specificity for detection of malignant pulmonary nodules primarily discovered with CT.