ABSTRACT
The long-term effects of coronavirus disease 2019 (COVID- 19) pneumonia on the lungs and pulmonary circulation require further characterization. We assessed progression of pathophysiological pulmonary changes during 1 year of follow-up of patients who had been hospitalized because of COVID-19. After discharge, recruited patients had up to four MRI examinations at a median of 6 (n=9), 12 (n=9), 25 (n=7) and 52 (n=3) weeks. Lung MRI examinations included: ultra-short echo time (UTE), dynamic contrastenhanced (DCE) lung perfusion, 129Xe diffusion weighted (DW-MRI), 129Xe ventilation and 129Xe 3D dissolved phase imaging. Nine patients (age 56 +/-9 years;six male) were recruited. Ventilation defect percentage and whole lung coefficient of variation of lung ventilation decreased significantly at 25 weeks (visit 3) compared with visit 1 at 6 weeks (p=0.010 and p=0.048). The UTE imaging indicated no evidence of lung scarring, and DW-MRI indicated normal lung microstructure across all visits. Dissolved phase xenon imaging showed that RBC:TP increased significantly at visits 2 and 3 compared with visit 1 (p=0.048). Median RBC:TP was abnormally low at all visits compared with reference age- and sex-matched data. An individual's RBC:TP was associated significantly and positively with an increase in their pulmonary blood volume (p=0.026). For patients with 52-week data available, one showed a continued improvement in RBC:TP;however, two of the patients maintained a low RBC:TP. In patients recovering from COVID-19, xenon gas transfer improves alongside pulmonary blood volume. Further work is needed to establish the proportion of post-COVID-19 patients who have longer-term impairment in xenon transfer and to correlate changes in lung MRI parameters with symptoms, lung function tests and other imaging modalities. Persistent impairment of xenon transfer might represent a physiological mechanism underlying ongoing symptoms in some patients and might indicate damage to the pulmonary microcirculation.
ABSTRACT
Accurate phenotyping of patients with pulmonary hypertension (PH) is an integral part of informing disease classification, treatment, and prognosis. The impact of lung disease on PH outcomes and response to treatment remains a challenging area with limited progress. Imaging with computed tomography (CT) plays an important role in patients with suspected PH when assessing for parenchymal lung disease, however, current assessments are limited by their semi-qualitative nature. Quantitative chest-CT (QCT) allows numerical quantification of lung parenchymal disease beyond subjective visual assessment. This has facilitated advances in radiological assessment and clinical correlation of a range of lung diseases including emphysema, interstitial lung disease, and coronavirus disease 2019 (COVID-19). Artificial Intelligence approaches have the potential to facilitate rapid quantitative assessments. Benefits of cross-sectional imaging include ease and speed of scan acquisition, repeatability and the potential for novel insights beyond visual assessment alone. Potential clinical benefits include improved phenotyping and prediction of treatment response and survival. Artificial intelligence approaches also have the potential to aid more focused study of pulmonary arterial hypertension (PAH) therapies by identifying more homogeneous subgroups of patients with lung disease. This state-of-the-art review summarizes recent QCT developments and potential applications in patients with PH with a focus on lung disease.