1903

Application of Pulmonary Perfusion Analysis in Phenotyping COVID-19 Cardiopulmonary Disease with Magnetic Resonance Imaging
Andrew C. Lancaster1, Yoko Kato2, Chia-Ying Liu3, Yoshimori Kassai3, Jaclyn Sesso2, Joao A. C. Lima2,4, and Bharath Ambale-Venkatesh4
1School of Medicine, Johns Hopkins University, Baltimore, MD, United States, 2Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 3Canon Medical Systems Corporation, Otawara, Japan, 4Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States

Synopsis

Keywords: Vessels, COVID-19, Perfusion

Pulmonary perfusion analysis of cardiac magnetic resonance imaging in patients with previous COVID-19 may help phenotype patients with advanced cardiopulmonary disease. Lung perfusion analysis was performed and results were correlated with clinical data in patients who were treated for COVID-19 at Johns Hopkins institutions. Older age and hospitalization for COVID-19 were found to be related to worse pulmonary perfusion, as measured by the perfusion parameter 50% max intensity, suggesting that one long-term sequela of severe COVID-19 that requires hospitalization, particularly in older patients, is a global bilateral decrease in the speed at which the lungs are perfused.

Introduction

Viral SARS-CoV-2, which causes COVID-19, has been implicated in multiple cardiopulmonary diseases1-6. Much remains to be learned regarding the long-term sequelae in patients who recover from COVID-19. Cardiac magnetic resonance imaging (MRI) studies have proven useful in the evaluation of patients with several cardiopulmonary diseases7-14, including COVID-19. Methods for assessing lung perfusion via MRI may aid in phenotyping patients with advanced disease15-21.
In a similar effort to that by Hueper et al.22, we sought to derive robust methodology for assessing quantitative pulmonary perfusion parameters from dynamic contrast-enhanced (DCE) MRI and to use this methodology to characterize sequelae of COVID-19 in individuals who receive similar care related to disease severity and duration in a single health care system. A history of pulmonary diseases affecting lung perfusion, such as COPD23, has been reported as a risk factor for hospitalization and poor prognosis/outcomes in COVID-19. Therefore, we sought to investigate the association between pulmonary perfusion parameters and clinical data, hypothesizing that patients who are hospitalized for COVID-19 illness, who are presumably sicker than patients who don’t require hospitalization, will have impaired lung perfusion post-COVID.

Methods

In a single-center analysis, patients who were diagnosed with COVID-19 after March 2020, who were 3-6 months post-discharge for confirmed COVID-19 illness at a Johns Hopkins institution, and who were 18 years of age or older were screened. Included patients underwent comprehensive cardiopulmonary imaging evaluation and phenotyping, including a cardiac MR imaging study (Canon Galan 3T scanner) with 4D lung perfusion images obtained after 0.04 mmol/kg Dotarem contrast at 5 mL/sec was administered.
Using QMass 8.1 (Medis Medical Imaging Systems, Leiden, Netherlands), pulmonary perfusion was assessed. A representative coronal image of the lungs around the anterior 1/3 of the chest was selected, such that the pulmonary artery trunk proximal to its bifurcation and the trachea could be visualized. Using QMass 8.1’s Time Signal Intensity analysis, the arterial input function (AIF) was assessed by creating a circular region of interest (ROI) around the middle of the pulmonary artery trunk blood pool, sparing adjacent areas of the vessel wall such that measurements would be robust against motion during the cardiac cycle and to avoid bias from the turbulence of blood flow. Pulmonary parenchymal ROIs were drawn on the lung peripheries to ensure they excluded the larger pulmonary arterial vasculature and allowed for positioning throughout the image acquisition phases. Right lung was consistently labeled as ROI1 and left lung as ROI2. ROIs were propagated to each phase in image acquisition. See figures 1A and 1B.
Perfusion data was automatically collected, and the time-signal intensity (SI) curves of each ROI were investigated (see figures 2A and 2B). The initial timing of the SI upslope was set as T0 and the timing of the end of the first-pass was set as TEnd. For each of the three ROIs, amplitude (au), max upslope (au/s), time max upslope (s), mean intensity (au), time 50% max (s), T0 intensity (au), baseline intensity (au), and relative upslope (%) were collected, where “au” stands for arbitrary unit.
Statistical regression analysis was performed to assess relationships between perfusion parameters (particularly, time to max upslope [s], time to 50% max intensity [s], and relative upslope [%]) and clinical characteristics – age, sex, BMI, whether hospitalized for COVID, whether reported any post-COVID symptoms, whether had a history of pulmonary disease prior to COVID diagnosis, and whether was a past smoker. T-tests were performed between the right and left lung perfusion parameters to assess for consistency in measurement.

Results

54 patients met inclusion criteria. Average age was 55.2 ± 12.4 years. Average BMI was 29.4 ± 6.0 kg/m2. 8 patients (15%) had a history of pulmonary disease, including COPD, asthma, or obstructive sleep apnea. For pulmonary artery blood pool, right lung, and left lung perfusion data, see Table 1.
There were no statistically significant differences between the lungs in terms of perfusion parameters: right relative upslope vs left relative upslope (t-test, p = 0.80); right time 50% max vs left time 50% max (t-test, p = 0.92); right time max upslope vs left time max upslope (t-test, p = 0.34). Regression analysis performed between perfusion parameters vs aforementioned clinical characteristics. Analysis revealed direct relationships between time to 50% max intensity and hospitalization [blood pool time 50% max and hospitalization, p = 0.000; right lung time 50% max and hospitalization, p = 0.000; and left lung time 50% max to hospitalization, p = 0.000] and between time to 50% max intensity and age [right lung time 50% max to age, p = 0.02; left lung time 50% max to age, p = 0.016].

Discussion/Conclusion

Older patients and those who required hospitalization for COVID-19 illness may have worse pulmonary perfusion, as measured by time to 50% max intensity on MRI, following their illness. This suggests that one long-term sequela of severe COVID-19 that requires hospitalization, particularly in older patients, is a global bilateral decrease in the speed at which the lungs are perfused. A limitation of our study is that we cannot definitively conclude whether the worse pulmonary perfusion is a consequence of COVID-19 or the reason for severe COVID-19 and/or hospitalization.

Acknowledgements

No acknowledgement found.

References

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Figures

Figures 1A and 1B: Example coronal slices from the same patient at different phases of acquisition, at peak intensity in the pulmonary artery trunk (A) and the following slice acquired (B). The red ROI with yellow center represents the Arterial Input Function (AIF) of the pulmonary artery trunk and is labeled “LV Endo”, per software. The orange ROI is ROI1 and represents the right lung. The purple ROI is ROI2 and represents the left lung.

Figures 2A and 2B: Example ROI Intensity vs Time plots for the patient in Figure 1. Signal intensity (SI) is plotted in arbitrary units (au) and time in seconds (s). The red curve (“LV Endo”) represents the pulmonary artery trunk blood pool. The orange curve is ROI1 and represents the right lung. The purple curve is ROI2 and represents the left lung. The initial timing of the SI upslope was set as T0 and the timing of the end of the first-pass was set as TEnd.

Table 1: Pulmonary artery trunk blood pool, right lung, and left lung perfusion data.

Proc. Intl. Soc. Mag. Reson. Med. 31 (2023)
1903
DOI: https://doi.org/10.58530/2023/1903