key: cord-0997165-j9quct3q
authors: Zhao, Fang; Zheng, Liyun; Shan, Fei; Dai, Yongming; Shen, Jie; Yang, Shuyi; Shi, Yuxin; Xue, Ke; Zhang, Zhiyong
title: Evaluation of Pulmonary Ventilation in COVID-19 Patients Using Oxygen-enhanced Three-Dimensional Ultrashort Echo Time Magnetic Resonance Imaging: A Preliminary Study
date: 2021-02-25
journal: Clin Radiol
DOI: 10.1016/j.crad.2021.02.008
sha: a725397466d7bb413ac0321e5ea999f7a9015aee
doc_id: 997165
cord_uid: j9quct3q

Aim To evaluate the lung function of coronavirus disease 2019 (COVID-19) patients using oxygen-enhanced (OE) ultrashort echo time (UTE) MRI. Materials and methods Forty-nine patients with COVID-19 were included in the study. The OE-MRI was based on a respiratory-gated three-dimensional (3D) radial UTE sequence. For each patient, the percent signal enhancement (PSE) map was calculated using the expression PSE = (S100% – S21%)/S21%, where S21% and S100% are signals acquired during room air and 100% oxygen inhalation, respectively. Agreement of lesion detectability between UTE-MRI and computed tomography (CT) was performed using the kappa test. The Mann–Whitney U-test was used to evaluate the difference in the mean PSE between mild-type COVID-19 and common-type COVID-19. Spearman’s test was used to assess the relationship between lesion mean PSE and lesion size. Furthermore, the Mann–Whitney U-test was used to evaluate the difference in region of interest (ROI) mean PSE between normal pulmonary parenchyma and lesions. The Kruskal–Wallis test was applied to test the difference in the mean PSE between different lesion types. Results CT and UTE-MRI reached good agreement in lesion detectability. Ventilation measures in mild-type patients (5.3 ± 5.5%) were significantly different from those in common-type patients (3 ± 3.9%). Besides, there was no significant correlation between lesion mean PSE and lesion size. The mean PSE of COVID-19 lesions (3.2 ± 4.9%) was significantly lower than that of the pulmonary parenchyma (5.4 ± 3.9%). No significant difference was found among different lesion types. Conclusion OE-UTE-MRI could serve as a promising method for the assessment of lung function or treatment management of COVID-19 patients.

As of 10 June 2020, coronavirus disease 2019 (COVID -19) has been confirmed in 7,039,918 people worldwide, invading over 160 countries, and carrying a mortality of approximately 6.9% 1 . Despite public health responses aimed at controlling the disease and delaying its spread, several countries have been confronted with a critical care crisis, and more countries will almost certainly follow 2 . To date, prior research has suggested that computed tomography (CT) has a high sensitivity in the diagnosis of COVID-19, 98%, compared to real-time polymerase chain reaction (RT-PCR), with a sensitivity of 71% 3 ; however, the prediction of the clinical course and prognosis of COVID-19 remains a challenge. There is an urgent need to identify patients at higher risk of developing acute respiratory failure so that they can be monitored closely and receive intervention treatment early.

The clinical course of COVID-19 often meets the Berlin definition of acute respiratory distress syndrome (ARDS); however, as a specific disease, the distinctive features of J o u r n a l P r e -p r o o f COVID-19 are severe hypoxaemia often associated with near-normal respiratory system compliance, which has seldom been seen in previous severe ARDS 4 . These severely hypoxaemic patients, despite sharing a single aetiology-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-may present with different phenotypes, and the degrees of ventilatory responsiveness may be quite different from one another 5 . In addition, accumulating evidence suggests that patients with COVID-19 have the potential for defective lung function and might suffer long-term impacts from the disease. For instance, a prospective longitudinal study of 90 patients with COVID-19-associated pneumonia found that 94% of discharged patients still had evidence of disease on their final CT 6 . This evidence included the persistence of ground-glass opacities (GGOs), which, in some patients, increased as they recovered enough to be discharged 6 . GGOs are hazy white opaque structures found on CT images, which are commonly seen in pneumonia and lung cancer. Therefore, it remains to be determined whether current COVID-19 patients will experience long-term dysfunction, similar to how some SARS patients experienced long-term lung function abnormalities 7 .

Once severe lung damage occurs, efforts should be made to suppress inflammation and manage the symptoms. As such, there is a need for a quantitative pulmonary function test that might be helpful in treatment decision-making and prognosis J o u r n a l P r e -p r o o f management. Functional magnetic resonance imaging (MRI) techniques, such as hyperpolarised helium 3 ( 3 He) MRI and, more recently, the less expensive alternative oxygen enhanced (OE) MRI, have been used to assess regional ventilation abnormalities of the lung. Three-dimensional radial ultrashort echo time (3D-UTE) MRI has shown promise as an OE-MRI method. This method does not require specialised multinuclear hardware or expensive specialty gas while providing full chest images of regional ventilation. With this technique, Kruger et al. performed whole-lung OE imaging in humans 8 and reported a comparison of ventilation defect distribution and signal intensity between OE-UTE-MRI and hyperpolarised 3 He MRI 9 .

Zha et al. further suggested that 3D-UTE MRI supports quantitative differentiation between diseased and healthy lungs with excellent test-retest repeatability 10 .

The goal of this study was to investigate the pulmonary ventilation of patients with COVID-19 with different severities, including both pulmonary parenchyma and lesions, using OE-UTE-MRI.

This study was approved by the institutional review board, and written informed consent from all patients was obtained. In this single-institution prospective study, J o u r n a l P r e -p r o o f patients diagnosed with COVID-19 according to the results of RT-PCR were recruited during the period from 25 March 2020 to 27 April 2020. Patients were divided into four groups based on the guidelines of the National Health Commission (7th edn; in Chinese) 11 : (1) mild type: mild clinical symptoms without pneumonia on imaging; (2) common type: fever, respiratory symptoms, and pneumonia manifestations on imaging. Common CT manifestations included GGOs, consolidation, and reticular and crazy-paving pattern 12 . Among these lesions, GGO is defined as hazy increased lung attenuation with preservation of bronchial and vascular margins. Consolidation is defined as opacification with obscuration of margins of vessels and airway walls 13 .

The crazy-paving pattern is characterised by scattered or diffuse ground-glass attenuation with superimposed interlobular septal thickening and intralobular lines 14 ;

(3) severe type: respiratory distress, respiratory rate ≥30 times/min; resting-state oxygen saturation ≤93%; PaO 2 /FiO 2 ≤300 mmHg; (4) critical type: respiratory failure requiring mechanical ventilation, shock, and other organ failure requiring intensive care unit (ICU) monitoring and treatment. Only mild-type and common-type COVID-19 patients were included in this study because most severe-and critical-type COVID-19 patients cannot endure MRI examinations, and some of them were transferred to the ICU immediately. In addition, all the patients included in this study only received basic supportive treatment without any special intervention during the CT and MRI The OE-UTE-MRI of the lungs was performed with a 3D-UTE sequence as in a previous study 15 . For this technique, the entire thoracic cavity was excited with a nonselective hard pulse, followed by the acquisition of a free induction decay (FID) signal instead of an echo (as in the case of most conventional clinical sequences), resulting in a centre-out radial encoding trajectory. Signal acquisition was initiated during the ramp-up stage of the encoding gradient to further reduce the effective echo time as well as potential susceptibility artefacts as a result of air-tissue boundaries in J o u r n a l P r e -p r o o f the lungs. The direction of the encoding gradient was incremented from one acquisition to another to cover the whole k-space in a "Koosh ball" pattern 16 . A total of 40,000 encoding directions were prescribed. To alleviate the effects of respiratory motion, the UTE sequence was interleaved with a navigator sequence to track the diaphragm displacement in the superior-inferior direction. The acquisition module was enabled only within a certain predetermined displacement range, during which 2,000 FIDs were collected each time. During reconstruction, the radial k-space data were first re-gridded onto Cartesian coordinates using a Kaiser-Bessel convolution kernel 17 . A 3D fast Fourier transform was subsequently performed to generate the final image.

For OE-UTE-MRI measurement, oxygen was delivered through a nonrebreather mask placed over the subject's nose and mouth. 3D-UTE was performed twice for each subject. The first was acquired during free-breathing with 21% oxygen (normoxic), while the second was acquired with 100% oxygen (hyperoxic). Two minutes of 100% oxygen inhalation was performed before the second UTE measurement to avoid the transit effect. The duration of whole MRI acquisition was about 20 minutes. Percent signal enhancement (PSE) was used to quantify pulmonary ventilation in this study. To avoid the negative impact from noise from the high-resolution normoxic and hyperoxic images on the quality of the PSE maps, the images were reconstructed at 1 cm resolution to improve the signal-to-noise (SNR) 8 . After that, the high-resolution normoxic and hyperoxic images were co-registered by rigid transform and B-spline J o u r n a l P r e -p r o o f symmetric normalisation (SyN) transform 18 with a mutual information metric using Advanced Normalisation Tools (http://stnava.github.io/ANTs). The high-resolution hyperoxic images were segmented automatically to produce a binary lung mask using ITK-SNAP (www.itksnap.org) 19 . After applying the deformation field from the registration and lung mask to the low-resolution data, the PSE map was computed as

where S 100% and S 21% represent the signal intensity of the hyperoxic and normoxic UTE images, respectively.

Lesion-based analysis was performed in consensus by two experienced radiologists. patients. As shown in Table 2 , CT and UTE-MRI reached good agreement in lesion detectability.

PSE analysis was frequently able to reveal the difference between patients with and without COVID-19-associated lesions (Fig. 1 ). As shown in Fig. 2 , the mean PSE between mild-type and common-type patients was significantly different (p=0.002).

The mean PSE of mild-type COVID-19 was 5.3±5.5% (mean±SD), while the mean PSE of common-type COVID-19 was 3±3.9% (mean±SD).

In this study, 142 out of 158 (89.9%) lesions could be readily visualised by PSE analysis (Fig. 3) . There was no significant correlation between lesion mean PSE and J o u r n a l P r e -p r o o f lesion size (p>0.05). The mean PSE of the pulmonary parenchyma ranged from 0.4% to 13.8% (mean±SD, 5.4±3.9%), while the mean PSE of the lesions was significantly lower (p<0.001) with a range of 0-9.4% and a mean of 3.2±4.9% (Fig. 4) . Furthermore, no significant difference was found among different lesion types (Fig. 5 ). pneumonia manifested as restrictive ventilation disorder and small airway obstruction 20 . In addition, as secondary ARDS carries risk factors affecting the prognosis of COVID-19 and causes high mortality 21 , evaluating lung function and monitoring the risk of ARDS are crucial not only for diagnosis but also for prognosis.

Although CT is able to visualise lung ventilation by using dual-energy scanners, exposure to ionising radiation can be detrimental, especially when repeated J o u r n a l P r e -p r o o f examinations are required. MRI has been used to evaluate different lung diseases with the advantage of detailed soft-tissue contrast and a lack of radiation 22 . In lung imaging, conventional MRI is challenging in that the extremely short T2* of the lung parenchyma, which is due to the low hydrogen proton density in this tissue, leads to very low signal intensity obtained in the lungs. In UTE-MRI, projection acquisition of the FID in conjunction with radial readout technically makes it possible to obtain a sufficient SNR with short TE and to reduce the sensitivity to motion 23 . Previously, 3D-UTE-MRI had good concordance with CT in assessing cystic fibrosis 24, 25 , quantifying lung parenchymal density, 26 and detecting pulmonary nodules 27 . The present study demonstrated that 3D-UTE-MRI was also in good agreement with CT for COVID-19

lesion detectability, which is in accordance with prior research 28 In the present lesion-based study, there was no significant correlation between the lesion mean PSE and lesion size, probably because only common-type patients were included in this part of the study. Compared with that of the normal pulmonary parenchyma, the mean PSE of the lesions was significantly reduced (p<0.001).

According to prior OE-MRI study, regional decreases in ventilation are common in pulmonary diseases, including lung cancer 34 , chronic obstructive pulmonary disease 35 , and asthma 36 . The measurements of regional ventilation defects from PSE maps depicts local to areas of structural abnormality. Four types of lesions were investigated in this study: GGOs, consolidations, GGOs with consolidation, and crazy-paving patterns. Consolidation is a homogeneous increase in pulmonary parenchymal attenuation that obscures the margins of vessels and airway walls 37 .

Various substances may fill the air space, including fluid, blood, pus, and cells, which probably cause a regional decrease in ventilation. A previous study suggested that consolidation was associated with impairment of gas exchange and lung stiffness 38 .

GGOs are caused by a partial filling of air spaces, a thickening of alveolar walls, and/or partial collapse of the alveoli 39 . Previous research also stated that an increased extent of GGOs may be correlated with a decrease in forced vital capacity 40 . According to a previous study, crazy-paving pattern lesion is related to progressive dyspnoea 41 . Thus, considering the structure and histology of COVID-19-related lesions, the result of lower mean PSE in all the lesions could be explained.

There was no significant difference between different lesion types in this study, likely attributable to a lack of consolidation (n=11) and the crazy-paving pattern (n=3). As shown in previous studies, more consolidation is found in patients with COVID-19 as the disease course progresses, and more consolidation lesions have been found in elderly patients (>50 years) than in younger patients 42 In summary, the regional and whole-lung oxygen enhancement observed on UTE-MRI in the present study suggested that OE-UTE-MRI could serve as a promising method in the evaluation of lung function in patients with COVID-19.

OE-UTE-MRI is feasible to stratify the severity of COVID-19 according to mean PSE, which may help guide the treatment, evaluate treatment response, predict prognosis and identify patients who require earlier intervention.

J o u r n a l P r e -p r o o f 

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Age, years (mean±SD)

All of the authors have made substantial contributions to all three of the following categories (1) substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; (2) drafting the article or revising it critically for important intellectual content

Guarantor of integrity of the entire study: Fang Zhao & Liyun Zheng 2. Study concepts and design: Zhiyong Zhang 3. Literature research: Fang Zhao, Liyun Zheng 4. Clinical studies: Fang Zhao, Liyun Zheng & Jie Shen 5. Experimental studies / data analysis: Shuyi Yang, Yuxin Shi & Ke Xue 6. Statistical analysis: Liyun Zheng & Yongming Dai 7. Manuscript preparation: Fang Zhao & Liyun Zheng

J o u r n a l P r e -p r o o f

☐ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.☒The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Three authors (Liyun Zheng, Yongming Dai and Ke Xue) are affiliated with United Imaging Healthcare J o u r n a l P r e -p r o o f