Anterior Pulmonary Ventilation Abnormalities in                     COVID-19

Yoon, Soon Ho; Kim, Minsuok

doi:10.1148/radiol.2020203043

Cited by 5 publications

(8 citation statements)

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“…CT pulmonary angiography with FAN ventilation modeling analysis in COVID-19 may reveal the origin of hypoxemia and help patient-specific management. The prior published case report of FAN ventilation modeling in a dyspneic patient with COVID-19 with pulmonary embolism on a CT pulmonary angiogram excluded as the cause, had a preferential anterior ventilation defect in 20.4% of the entire lung but with a percentage pneumonia involvement of only 0.9% is supportive of this suggestion [ 12 ]. Additionally pulmonary microvascular thrombosis resulting in perfusion defects assessed using dual energy CT has also previously been reported to be useful in patients with COVID-19 [ 27 ] and this combined with FAN ventilation modeling may help assess whether vascular pathology or intrapulmonary shunting or a combination of both may be the cause of hypoxemia, and whether aerated or non-aerated lungs with normal or impaired ventilation dominate when considering positive pressure ventilation and whether supine or prone positioning are appropriate.…”

Section: Discussionmentioning

confidence: 84%

Section: Plos Onementioning

confidence: 80%

“…The former subset has been regarded as having normal ventilation, but it is currently unknown whether ventilation is normal or impaired in the aerated lung on CT. Functional pulmonary ventilation imaging would be a direct way to observe pulmonary ventilation but has been rarely attempted in COVID-19 because the procedures required for functional imaging, specifically because the resulting aerosolization increases the risk of infection of staff [ 9 ]. Recent studies based on conventional CT imaging without using inhaled gas have demonstrated that regional ventilation computed by mathematical modeling correlates with conventional pulmonary ventilation imaging [ 10 , 11 ] and is applicable to COVID-19 [ 12 ]. This study aimed to investigate ventilation abnormalities on chest CT images in COVID-19.…”

Section: Introductionmentioning

confidence: 99%

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Impaired pulmonary ventilation beyond pneumonia in COVID-19: A preliminary observation

et al. 2022

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Background Coronavirus disease 2019 (COVID-19) may severely impair pulmonary function and cause hypoxia. However, the association of COVID-19 pneumonia on CT with impaired ventilation remains unexplained. This pilot study aims to demonstrate the relationship between the radiological findings on COVID-19 CT images and ventilation abnormalities simulated in a computational model linked to the patients’ symptoms. Methods Twenty-five patients with COVID-19 and four test-negative healthy controls who underwent a baseline non-enhanced CT scan: 7 dyspneic patients, 9 symptomatic patients without dyspnea, and 9 asymptomatic patients were included. A 2D U-Net-based CT segmentation software was used to quantify radiological futures of COVID-19 pneumonia. The CT image-based full-scale airway network (FAN) flow model was employed to assess regional lung ventilation. Functional and radiological features were compared across groups and correlated with the clinical symptoms. Heterogeneity in ventilation distribution and ventilation defects associated with the pneumonia and the patients’ symptoms were assessed. Results Median percentage ventilation defects were 0.2% for healthy controls, 0.7% for asymptomatic patients, 1.2% for symptomatic patients without dyspnea, and 11.3% for dyspneic patients. The median of percentage pneumonia was 13.2% for dyspneic patients and 0% for the other groups. Ventilation defects preferentially affected the posterior lung and worsened with increasing pneumonia linearly (y = 0.91x + 0.99, R2 = 0.73) except for one of the nine dyspneic patients who had disproportionally large ventilation defects (7.8% of the entire lung) despite mild pneumonia (1.2%). The symptomatic and dyspneic patients showed significantly right-skewed ventilation distributions (symptomatic without dyspnea: 0.86 ± 0.61, dyspnea 0.91 ± 0.79) compared to the patients without symptom (0.45 ± 0.35). The ventilation defect analysis with the FAN model provided a comparable diagnostic accuracy to the percentage pneumonia in identifying dyspneic patients (area under the receiver operating characteristic curve, 0.94 versus 0.96). Conclusions COVID-19 pneumonia segmentations from CT scans are accompanied by impaired pulmonary ventilation preferentially in dyspneic patients. Ventilation analysis with CT image-based computational modelling shows it is able to assess functional impairment in COVID-19 and potentially identify one of the aetiologies of hypoxia in patients with COVID-19 pneumonia.

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Section: Discussionmentioning

confidence: 84%

Section: Plos Onementioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

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Impaired pulmonary ventilation beyond pneumonia in COVID-19: A preliminary observation

et al. 2022

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“…The Korean Society of Thoracic Radiology (KSTR) has been much involved in professional activities designed to address the challenges posed by COVID-19 since February 2020, and also participates in academic research, 1 4 9 10 11 12 13 14 15 16 shares representative cases, 17 18 issues imaging guidelines, 19 and provides COVID-19 education. 20 In addition to these activities, the KSTR recently constructed a nation-wide COVID-19 database and imaging repository, referred to the Korean imaging cohort of COVID-19 (KICC-19) based on the collaborative efforts of its members.…”

Section: Introductionmentioning

confidence: 99%

Establishment of a Nationwide Korean Imaging Cohort of Coronavirus Disease 2019

et al. 2020

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Background The Korean Society of Thoracic Radiology (KSTR) recently constructed a nation-wide coronavirus disease 2019 (COVID-19) database and imaging repository, referred to the Korean imaging cohort of COVID-19 (KICC-19) based on the collaborative efforts of its members. The purpose of this study was to provide a summary of the clinico-epidemiological data and imaging data of the KICC-19. Methods The KSTR members at 17 COVID-19 referral centers retrospectively collected imaging data and clinical information of consecutive patients with reverse transcription polymerase chain reaction-proven COVID-19 in respiratory specimens from February 2020 through May 2020 who underwent diagnostic chest computed tomography (CT) or radiograph in each participating hospital. Results The cohort consisted of 239 men and 283 women (mean age, 52.3 years; age range, 11–97 years). Of the 522 subjects, 201 (38.5%) had an underlying disease. The most common symptoms were fever (n = 292) and cough (n = 245). The 151 patients (28.9%) had lymphocytopenia, 86 had (16.5%) thrombocytopenia, and 227 patients (43.5%) had an elevated CRP at admission. The 121 (23.4%) needed nasal oxygen therapy or mechanical ventilation (n = 38; 7.3%), and 49 patients (9.4%) were admitted to an intensive care unit. Although most patients had cured, 21 patients (4.0%) died. The 465 (89.1%) subjects underwent a low to standard-dose chest CT scan at least once during hospitalization, resulting in a total of 658 CT scans. The 497 subjects (95.2%) underwent chest radiography at least once during hospitalization, which resulted in a total of 1,475 chest radiographs. Conclusion The KICC-19 was successfully established and comprised of 658 CT scans and 1,475 chest radiographs of 522 hospitalized Korean COVID-19 patients. The KICC-19 will provide a more comprehensive understanding of the clinical, epidemiological, and radiologic characteristics of patients with COVID-19.

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“…However, in this study etiology for each attenuation variations in pixels evaluation are expected to be associated with pathological changes in the alveolar levels, however, it is very difficult to correlate the pixel variation in millimeter resolution with alveolar changes due to COVID-19 pneumonia. Understanding of the chest CT radiological findings and correlation with bilateral diffuse alveolar damage requires further work that can be explained through numerical modeling [32] as well as pathological and histological findings are expected to the door to a new simulation platform and provide fundamental insight into COVID-19 pneumonia. In this respect, functional imaging…”

Section: Discussionmentioning

confidence: 99%

Pixel-based analysis of pulmonary changes on CT lung images due to COVID-19 pneumonia

Soya

Ekenel

Savaş

et al. 2022

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Objectives: Computed tomography (CT) plays a complementary role in the diagnosis of the pneumonia-burden of COVID-19 disease. However, the low contrast of areas of inflammation on CT images, areas of infection are difficult to identify. The purpose of this study is to develop a post-image-processing method for quantitative analysis of COVID-19 pneumonia-related changes in CT attenuation values using a pixel-based analysis rather than more commonly used clustered focal pneumonia volumes. The COVID-19 pneumonia burden is determined by experienced radiologists in the clinic. Previous AI software was developed for the measurement of COVID-19 lesions based on the extraction of local pneumonia features. In this respect, changes in the pixel levels beyond the clusters may be overlooked by deep learning algorithms. The proposed technique focuses on the quantitative measurement of COVID-19 related pneumonia over the entire lung in pixel-by-pixel fashion rather than only clustered focal pneumonia volumes. Material and Methods: Fifty COVID-19 and 50 age-matched negative control patients were analyzed using the proposed technique and commercially available artificial intelligence (AI) software. The %pneumonia was calculated using the relative volume of parenchymal pixels within an empirically defined CT density range, excluding pulmonary airways, vessels, and fissures. One-way ANOVA analysis was used to investigate the statistical difference between lobar and whole lung %pneumonia in the negative control and COVID-19 cohorts. Results: The threshold of high-and-low CT attenuation values related to pneumonia caused by COVID-19 were found to be between ₋642.4 HU and 143 HU. The %pneumonia of the whole lung, left upper, and lower lobes were 8.1 ± 4.4%, 6.1 ± 4.5, and 11.3 ± 7.3% for the COVID-19 cohort, respectively, and statistically different (P < 0.01). Additionally, the pixel-based methods correlate well with existing AI methods and are approximately four times more sensitive to pneumonia particularly at the upper lobes compared with commercial software in COVID-19 patients (P < 0.01). Conclusion: Pixel-by-pixel analysis can accurately assess pneumonia in COVID-19 patients with CT. Pixel-based techniques produce more sensitive results than AI techniques. Using the proposed novel technique, %pneumonia could be quantitatively calculated not only in the clusters but also in the whole lung with an improved sensitivity by a factor of four compared to AI-based analysis. More significantly, pixel-by-pixel analysis was more sensitive to the upper lobe pneumonia, while AI-based analysis overlooked the upper lung pneumonia region. In the future, this technique can be used to investigate the efficiency of vaccines and drugs and post COVID-19 effects.

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Anterior Pulmonary Ventilation Abnormalities in COVID-19

Cited by 5 publications

References 3 publications

Impaired pulmonary ventilation beyond pneumonia in COVID-19: A preliminary observation

Impaired pulmonary ventilation beyond pneumonia in COVID-19: A preliminary observation

Establishment of a Nationwide Korean Imaging Cohort of Coronavirus Disease 2019

Pixel-based analysis of pulmonary changes on CT lung images due to COVID-19 pneumonia

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