66 Heart and lung tissue characterization of COVID-19 pneumonia by T1 and T2 mapping magnetic resonance imaging

Aims Coronavirus disease 2019 (COVID-19) is a respiratory tract infection which can lead to systemic involvement including myocardial injury, severe respiratory failure and death. Magnetic resonance imaging (MRI) could potentially offer advantages in providing tissue characterization of lung parenchyma and heart muscle in COVID-19. The aim of the present study was to describe data on heart and lung MRI in a cohort of patients hospitalized due to COVID-19 pneumonia. Methods and results n = 11 patients hospitalized with COVID-19 pneumonia underwent a comprehensive MRI examinations including lung and heart tissue mapping, findings were compared to those of an age- and sex-matched cohort of n = 11 individuals. Lung native T1 and T2 mapping assessments were performed by drawing a circular region of interest (ROI) with diameter of 2 cm in the parenchyma visualized from the cardiac four chamber long axis-oriented slice; vessels and areas of pleural effusion were carefully excluded. Myocardial native T1 and T2 mapping were assessed by drawing a ROI within the midventricular left ventricular (LV) septum. No patients had previous history of cardiovascular disease (including known coronary artery disease, heart failure, cardiomyopathy, atrial fibrillation). As compared to controls, patients with COVID-19 had similar cardiac function, higher mid-septum myocardial native T1 (1028 ms vs. 985, P = 0.05) and significantly higher lung native T1 and T2 within affected areas (1375 ms vs. 1201 ms, P = 0.016 and 70 ms vs. 30 ms, P < 0.001 respectively), whereas non-significant differences were observed between remote lung areas of patients and controls (1238 ms vs. 1152 ms, P = 0.088 and 29 ms vs. 33 ms, P = 0.797 respectively). No significant associations were observed between cardiac and lung mapping findings. Conclusions In our cohort of patients with COVID-19, T1 and T2 mapping lung MRI identified pneumonia related abnormalities as compared to healthy controls, likely representing oedema and ongoing inflammation at the lung site. Myocardial native T1 was elevated suggesting the presence of cardiac involvement. A comprehensive MRI examination can be potentially used to assess multiorgan involvement in COVID-19.

Lung MRI with hyperpolarised gases: current & future clinical perspectives

The use of pulmonary MRI in a clinical setting has historically been limited. Whilst CT remains the gold-standard for structural lung imaging in many clinical indications, technical developments in ultrashort and zero echo time MRI techniques are beginning to help realise non-ionising structural imaging in certain lung disorders. In this invited review, we discuss a complementary technique – hyperpolarised (HP) gas MRI with inhaled 3He and 129Xe – a method for functional and microstructural imaging of the lung that has great potential as a clinical tool for early detection and improved understanding of pathophysiology in many lung diseases. HP gas MRI now has the potential to make an impact on clinical management by enabling safe, sensitive monitoring of disease progression and response to therapy. With reference to the significant evidence base gathered over the last two decades, we review HP gas MRI studies in patients with a range of pulmonary disorders, including COPD/emphysema, asthma, cystic fibrosis, and interstitial lung disease. We provide several examples of our experience in Sheffield of using these techniques in a diagnostic clinical setting in challenging adult and paediatric lung diseases.

Deep learning-based segmentation of the lung in MR-images acquired by a stack-of-spirals trajectory at ultra-short echo-times

Background Functional lung MRI techniques are usually associated with time-consuming post-processing, where manual lung segmentation represents the most cumbersome part. The aim of this study was to investigate whether deep learning-based segmentation of lung images which were scanned by a fast UTE sequence exploiting the stack-of-spirals trajectory can provide sufficiently good accuracy for the calculation of functional parameters. Methods In this study, lung images were acquired in 20 patients suffering from cystic fibrosis (CF) and 33 healthy volunteers, by a fast UTE sequence with a stack-of-spirals trajectory and a minimum echo-time of 0.05 ms. A convolutional neural network was then trained for semantic lung segmentation using 17,713 2D coronal slices, each paired with a label obtained from manual segmentation. Subsequently, the network was applied to 4920 independent 2D test images and results were compared to a manual segmentation using the Sørensen–Dice similarity coefficient (DSC) and the Hausdorff distance (HD). Obtained lung volumes and fractional ventilation values calculated from both segmentations were compared using Pearson’s correlation coefficient and Bland Altman analysis. To investigate generalizability to patients outside the CF collective, in particular to those exhibiting larger consolidations inside the lung, the network was additionally applied to UTE images from four patients with pneumonia and one with lung cancer. Results The overall DSC for lung tissue was 0.967 ± 0.076 (mean ± standard deviation) and HD was 4.1 ± 4.4 mm. Lung volumes derived from manual and deep learning based segmentations as well as values for fractional ventilation exhibited a high overall correlation (Pearson’s correlation coefficent = 0.99 and 1.00). For the additional cohort with unseen pathologies / consolidations, mean DSC was 0.930 ± 0.083, HD = 12.9 ± 16.2 mm and the mean difference in lung volume was 0.032 ± 0.048 L. Conclusions Deep learning-based image segmentation in stack-of-spirals based lung MRI allows for accurate estimation of lung volumes and fractional ventilation values and promises to replace the time-consuming step of manual image segmentation in the future.

COVID-19 pathophysiology may be driven by an imbalance in the renin-angiotensin-aldosterone system

SARS-CoV-2 uses ACE2, an inhibitor of the Renin-Angiotensin-Aldosterone System (RAAS), for cellular entry. Studies indicate that RAAS imbalance worsens the prognosis in COVID-19. We present a consecutive retrospective COVID-19 cohort with findings of frequent pulmonary thromboembolism (17%), high pulmonary artery pressure (60%) and lung MRI perfusion disturbances. We demonstrate, in swine, that infusing angiotensin II or blocking ACE2 induces increased pulmonary artery pressure, reduces blood oxygenation, increases coagulation, disturbs lung perfusion, induces diffuse alveolar damage, and acute tubular necrosis compared to control animals. We further demonstrate that this imbalanced state can be ameliorated by infusion of an angiotensin receptor blocker and low-molecular-weight heparin. In this work, we show that a pathophysiological state in swine induced by RAAS imbalance shares several features with the clinical COVID-19 presentation. Therefore, we propose that severe COVID-19 could partially be driven by a RAAS imbalance.

Lung MRI in Children: The Road Less Travelled

Magnetic resonance imaging (MRI) of the lungs is one of the most underutilized imaging modality when it comes to imaging of thoracic diseases in children. This is largely due to less-than-optimal image quality and multiple technical challenges involved with MRI of the lungs. Advances in MRI technology along with increased awareness about optimization of MR protocol have led to it being viewed as a feasible option for evaluation of various chest diseases in children. This short review article takes the reader to the road less travelled to explore newer horizons for applications of this rapidly evolving magnetic resonance technique in the field of thoracic diseases in children.