CT-based Airway Flow Model to Assess Ventilation in Chronic Obstructive Pulmonary Disease: A Pilot Study

Exploratory Study on COPD Phenotypes and their Progression: Integrating SPECT and qCT Imaging Analysis

Background

The objective of this study is to understand chronic obstructive pulmonary disease (COPD) phenotypes and their progressions by quantifying heterogeneities of lung ventilation from the single photon emission computed tomography (SPECT) images and establishing associations with the quantitative computed tomography (qCT) imaging-based clusters and variables.

Methods

Eight COPD patients completed a longitudinal study of three visits with intervals of about a year. CT scans of these subjects at residual volume, functional residual capacity, and total lung capacity were taken for all visits. The functional and structural qCT-based variables were derived, and the subjects were classified into the qCT-based clusters. In addition, the SPECT variables were derived to quantify the heterogeneity of lung ventilation. The correlations between the key qCT-based variables and SPECT-based variables were examined.

Results

The SPECT-based coefficient of variation (CVTotal), a measure of ventilation heterogeneity, showed strong correlations (|r| ≥ 0.7) with the qCT-based functional small airway disease percentage (fSAD%Total) and emphysematous tissue percentage (Emph%Total) in the total lung on cross-sectional data. As for the two-year changes, the SPECT-based maximum tracer concentration (TCmax), a measure of hot spots, exhibited strong negative correlations with fSAD%Total, Emph%Total, average airway diameter in the left upper lobe, and airflow distribution in the middle and lower lobes.

Conclusion

Small airway disease is highly associated with the heterogeneity of ventilation in COPD lungs. TCmax is a more sensitive functional biomarker for COPD progression than CVTotal. Besides fSAD%Total and Emph%Total, segmental airways narrowing and imbalanced ventilation between upper and lower lobes may contribute to the development of hot spots over time.

Functional Impairment in Small Airways Associated With the Breathlessness Symptoms in Long-Coronavirus Disease

PURPOSE

This study aimed to determine the association between functional impairment in small airways and symptoms of dyspnea in patients with Long-coronavirus disease (COVID), using imaging and computational modeling analysis.

PATIENTS AND METHODS

Thirty-four patients with Long-COVID underwent thoracic computed tomography and hyperpolarized Xenon-129 magnetic resonance imaging (HP Xe MRI) scans. Twenty-two answered dyspnea-12 questionnaires. We used a computed tomography-based full-scale airway network (FAN) flow model to simulate pulmonary ventilation. The ventilation distribution projected on a coronal plane and the percentage lobar ventilation modeled in the FAN model were compared with the HP Xe MRI data. To assess the ventilation heterogeneity in small airways, we calculated the fractal dimensions of the impaired ventilation regions in the HP Xe MRI and FAN models.

RESULTS

The ventilation distribution projected on a coronal plane showed an excellent resemblance between HP Xe MRI scans and FAN models (structure similarity index: 0.87 ± 0.04). In both the image and the model, the existence of large clustered ventilation defects was not identifiable regardless of dyspnea severity. The percentage lobar ventilation of the HP Xe MRI and FAN model showed a strong correlation (ρ = 0.63, P < 0.001). The difference in the fractal dimension of impaired ventilation zones between the low and high dyspnea-12 score groups was significant (HP Xe MRI: 1.97 [1.89 to 2.04] and 2.08 [2.06 to 2.14], P = 0.005; FAN: 2.60 [2.59 to 2.64] and 2.64 [2.63 to 2.65], P = 0.056).

CONCLUSIONS

This study has identified a potential association of small airway functional impairment with breathlessness in Long-COVID, using fractal analysis of HP Xe MRI scans and FAN models.

PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation

Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization and quantification of regional lung ventilation; however, these techniques require specialized equipment and exogenous contrast, limiting clinical adoption. Physiologically-informed techniques to map proton (¹H)-MRI ventilation have been proposed. These approaches have demonstrated moderate correlation with hyperpolarized gas MRI. Recently, deep learning (DL) has been used for image synthesis applications, including functional lung image synthesis. Here, we propose a 3D multi-channel convolutional neural network that employs physiologically-informed ventilation mapping and multi-inflation structural ¹H-MRI to synthesize 3D ventilation surrogates (PhysVENeT). The dataset comprised paired inspiratory and expiratory ¹H-MRI scans and corresponding hyperpolarized gas MRI scans from 170 participants with various pulmonary pathologies. We performed fivefold cross-validation on 150 of these participants and used 20 participants with a previously unseen pathology (post COVID-19) for external validation. Synthetic ventilation surrogates were evaluated using voxel-wise correlation and structural similarity metrics; the proposed PhysVENeT framework significantly outperformed conventional ¹H-MRI ventilation mapping and other DL approaches which did not utilize structural imaging and ventilation mapping. PhysVENeT can accurately reflect ventilation defects and exhibits minimal overfitting on external validation data compared to DL approaches that do not integrate physiologically-informed mapping.

Quantitative Imaging Metrics for the Assessment of Pulmonary Pathophysiology: An Official American Thoracic Society and Fleischner Society Joint Workshop Report

Multiple thoracic imaging modalities have been developed to link structure to function in the diagnosis and monitoring of lung disease. Volumetric computed tomography (CT) renders three-dimensional maps of lung structures and may be combined with positron emission tomography (PET) to obtain dynamic physiological data. Magnetic resonance imaging (MRI) using ultrashort-echo time (UTE) sequences has improved signal detection from lung parenchyma; contrast agents are used to deduce airway function, ventilation-perfusion-diffusion, and mechanics. Proton MRI can measure regional ventilation-perfusion ratio. Quantitative imaging (QI)-derived endpoints have been developed to identify structure-function phenotypes, including air-blood-tissue volume partition, bronchovascular remodeling, emphysema, fibrosis, and textural patterns indicating architectural alteration. Coregistered landmarks on paired images obtained at different lung volumes are used to infer airway caliber, air trapping, gas and blood transport, compliance, and deformation. This document summarizes fundamental "good practice" stereological principles in QI study design and analysis; evaluates technical capabilities and limitations of common imaging modalities; and assesses major QI endpoints regarding underlying assumptions and limitations, ability to detect and stratify heterogeneous, overlapping pathophysiology, and monitor disease progression and therapeutic response, correlated with and complementary to, functional indices. The goal is to promote unbiased quantification and interpretation of in vivo imaging data, compare metrics obtained using different QI modalities to ensure accurate and reproducible metric derivation, and avoid misrepresentation of inferred physiological processes. The role of imaging-based computational modeling in advancing these goals is emphasized. Fundamental principles outlined herein are critical for all forms of QI irrespective of acquisition modality or disease entity.

Gas exchange and ventilation imaging of healthy and COPD subjects using hyperpolarized xenon-129 MRI and a 3D alveolar gas-exchange model

OBJECTIVES

To investigate the utility of hyperpolarized xenon-129 (HPX) gas-exchange magnetic resonance imaging (MRI) and modeling in a chronic obstructive pulmonary disease (COPD) cohort in comparison to a minimal CT-diagnosed emphysema (MCTE) cohort and a healthy cohort.

METHODS

A total of 25 subjects were involved in this study including COPD (n = 8), MCTE (n = 3), and healthy (n = 14) subjects. The COPD subjects were scanned using HPX ventilation, gas-exchange MRI, and volumetric CT. The healthy subjects were scanned using the same HPX gas-exchange MRI protocol with 9 of them scanned twice, 3 weeks apart. The coefficient of variation (CV) was used to quantify image heterogeneities. A three-dimensional computational fluid dynamic (CFD) model of gas exchange was used to derive functional volumes of pulmonary tissue, capillaries, and veins.

RESULTS

The CVs of gas distributions in the images showed that there was a statistically significant difference between the COPD and healthy subjects (p < 0.0001). The functional volumes of pulmonary tissue, capillaries, and veins were significantly lower in the subjects with COPD than in the healthy subjects (p < 0.001). The functional volume of pulmonary tissue was found to be (i) statistically different between the healthy and MCTE groups (p = 0.02) and (ii) dependent on the age of the subjects in the healthy group (p = 0.0008) while their CVs (p = 0.13) were not.

CONCLUSION

The novel HPX gas-exchange MRI and CFD model distinguished the healthy cohort from the MCTE and COPD cohorts. The proposed technique also showed that the functional volume of pulmonary tissue decreases with aging in the healthy group.

KEY POINTS

• The ventilation and gas-exchange imaging with hyperpolarized xenon-129 MRI has enabled the identification of gas-exchange variation between COPD and healthy groups. • This novel technique was promising to be sensitive to minimal CT-diagnosed emphysema and age-related changes in gas-exchange parameter in a small pilot cohort.

Lung Abnormalities Detected with Hyperpolarized 129Xe MRI in Patients with Long COVID

Background Post-COVID-19 condition encompasses symptoms following COVID-19 infection that linger at least 4 weeks after the end of active infection. Symptoms are wide ranging, but breathlessness is common. Purpose To determine if the previously described lung abnormalities seen on hyperpolarized (HP) pulmonary xenon 129 (129Xe) MRI scans in participants with post-COVID-19 condition who were hospitalized are also present in participants with post-COVID-19 condition who were not hospitalized. Materials and Methods In this prospective study, nonhospitalized participants with post-COVID-19 condition (NHLC) and posthospitalized participants with post-COVID-19 condition (PHC) were enrolled from June 2020 to August 2021. Participants underwent chest CT, HP 129Xe MRI, pulmonary function testing, and the 1-minute sit-to-stand test and completed breathlessness questionnaires. Control subjects underwent HP 129Xe MRI only. CT scans were analyzed for post-COVID-19 interstitial lung disease severity using a previously published scoring system and full-scale airway network (FAN) modeling. Analysis used group and pairwise comparisons between participants and control subjects and correlations between participant clinical and imaging data. Results A total of 11 NHLC participants (four men, seven women; mean age, 44 years ± 11 [SD]; 95% CI: 37, 50) and 12 PHC participants (10 men, two women; mean age, 58 years ±10; 95% CI: 52, 64) were included, with a significant difference in age between groups (P = .05). Mean time from infection was 287 days ± 79 (95% CI: 240, 334) and 143 days ± 72 (95% CI: 105, 190) in NHLC and PHC participants, respectively. NHLC and PHC participants had normal or near normal CT scans (mean, 0.3/25 ± 0.6 [95% CI: 0, 0.63] and 7/25 ± 5 [95% CI: 4, 10], respectively). Gas transfer (Dlco) was different between NHLC and PHC participants (mean Dlco, 76% ± 8 [95% CI: 73, 83] vs 86% ± 8 [95% CI: 80, 91], respectively; P = .04), but there was no evidence of other differences in lung function. Mean red blood cell-to-tissue plasma ratio was different between volunteers (mean, 0.45 ± 0.07; 95% CI: 0.43, 0.47]) and PHC participants (mean, 0.31 ± 0.10; 95% CI: 0.24, 0.37; P = .02) and between volunteers and NHLC participants (mean, 0.37 ± 0.10; 95% CI: 0.31, 0.44; P = .03) but not between NHLC and PHC participants (P = .26). FAN results did not correlate with Dlco) or HP 129Xe MRI results. Conclusion Nonhospitalized participants with post-COVID-19 condition (NHLC) and posthospitalized participants with post-COVID-19 condition (PHC) showed hyperpolarized pulmonary xenon 129 MRI and red blood cell-to-tissue plasma abnormalities, with NHLC participants demonstrating lower gas transfer than PHC participants despite having normal CT findings. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Parraga and Matheson in this issue.

Assessing liver fibrosis distribution through liver elasticity estimates obtained using a biomechanical model of respiratory motion with magnetic resonance elastography

Objective. This study aimed to produce a three-dimensional liver elasticity map using the finite element method (FEM) and respiration-induced motion captured by T1-weighted magnetic resonance images (FEM-E-map) and to evaluate whether FEM-E-maps can be an imaging biomarker comparable to magnetic resonance elastography (MRE) for assessing the distribution and severity of liver fibrosis. Approach. We enrolled 14 patients who underwent MRI and MRE. T1-weighted MR images were acquired during shallow inspiration and expiration breath-holding, and the displacement vector field (DVF) between two images was calculated using deformable image registration. FEM-E-maps were constructed using FEM and DVF. First, three Poisson’s ratio settings (0.45, 0.49, and 0.499995) were validated and optimized to minimize the difference in liver elasticity between the FEM-E-map and MRE. Then, the whole and regional liver elasticity values estimated using FEM-E-maps were compared with those obtained from MRE using Pearson’s correlation coefficients. Spearman rank correlations and chi-square histograms were used to compare the voxel-level elasticity distribution. Main results. The optimal Poisson’s ratio was 0.49. Whole liver elasticity estimated using FEM-E-maps was strongly correlated with that measured using MRE (r = 0.96). For regional liver elasticity, the correlation was 0.84 for the right lobe and 0.82 for the left lobe. Spearman analysis revealed a moderate correlation for the voxel-level elasticity distribution between FEM-E-maps and MRE (0.61 ± 0.10). The small chi-square distances between the two histograms (0.11 ± 0.07) indicated good agreement. Significance. FEM-E-maps represent a potential imaging biomarker for visualizing the distribution of liver fibrosis using only T1-weighted images obtained with a common MR scanner, without any additional examination or special elastography equipment. However, additional studies including comparisons with biopsy findings are required to verify the reliability of this method for clinical application.

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

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.

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

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.

Repeatability of dynamic 3D phase-resolved functional lung (PREFUL) ventilation MR Imaging in patients with chronic obstructive pulmonary disease and healthy volunteers

BACKGROUND

A previous study has demonstrated the feasibility of 3D phase-resolved functional lung (PREFUL) MRI in healthy volunteers and patients with chronic pulmonary disease. Before clinical use, the repeatability of the ventilation parameters derived from 3D PREFUL MRI must be determined.

PURPOSE

To evaluate repeatability of 3D PREFUL and to compare with pulmonary functional lung testing (PFT).

STUDY TYPE

Prospective.

POPULATION

Fifty-three healthy subjects and 13 patients with chronic obstructive pulmonary disease (COPD).

FIELD STRENGTH/SEQUENCE

A prototype 3D stack-of-stars spoiled-gradient-echo sequence at 1.5 T.

ASSESSMENT

Study participants underwent repeated MRI examination (median time interval between scans COPD/healthy subjects [interquartile range]: 7/0 days [6-8/0-0 days]) and one PFT carried out at the time of the baseline MRI. For 3D PREFUL, regional ventilation (RVent) and flow-volume loops were computed and rated by cross-correlation (CC). Also, ventilation time-to-peak (VTTP) was computed. Ventilation defect percentage (VDP) maps were obtained for RVent and CC.

STATISTICAL TESTS

Repeatability of 3D PREFUL parameters was evaluated using Bland-Altman analysis, coefficient of variation (COV) and intraclass correlation coefficient (ICC). The relation between 3D PREFUL and PFT measures (forced expiratory volume in 1 second (FEV₁ ) and forced vital capacity (FVC) was assessed using the Pearson correlation coefficient (r).

RESULTS

In healthy subjects and COPD patients, no significant bias (all P range: 0.09-0.77) and a moderate to good repeatability of RVent, VTTP, and VDP_RVent were found (COV range: 0.1%-18.2%, ICC range: 0.51-0.88). For CC and VDP_CC moderate repeatability was found (COV range: 0.6%-43.6%, ICC: 0.38-0.60). CC, VDP_RVent , and VDP_CC showed a good correlation with FEV₁ (all |r| > 0.58, all P < 0.05) and FEV₁ /FVC ratio (all |r| > 0.62, all P < 0.05).

DATA CONCLUSION

3D PREFUL provided a good repeatability of RVent, VTTP, and VDP_RVent and moderate repeatability of CC and VDP_CC in healthy volunteers and COPD patients, and correlated well with FEV₁ and FEV₁ /FVC.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 2.

Flow Volume Loop and Regional Ventilation Assessment Using Phase-Resolved Functional Lung (PREFUL) MRI: Comparison With 129 Xenon Ventilation MRI and Lung Function Testing

BACKGROUND

Regional flow volume loop ventilation-weighted noncontrast-enhanced proton lung MRI in free breathing has emerged as a novel technique for assessment of regional lung ventilation, but has yet not been validated with ¹²⁹ Xenon MRI (¹²⁹ Xe-MRI), a direct visualization of ventilation in healthy volunteers, cystic fibrosis (CF), and chronic obstructive pulmonary disease (COPD) patients.

PURPOSE

To compare regional ventilation and regional flow volume loops measured by noncontrast-enhanced ventilation-weighted phase-resolved functional lung MRI (PREFUL-MRI) with ¹²⁹ Xe-MRI ventilation imaging and with lung function test parameters.

STUDY TYPE

Retrospective study.

POPULATION

Twenty patients with COPD, eight patients with CF, and six healthy volunteers.

FIELD STRENGTH/SEQUENCE

PREFUL and ¹²⁹ Xe-MRI gradient echo sequences were acquired at 1.5T.

ASSESSMENT

Coronal slices of PREFUL-MRI (free breathing) and ¹²⁹ Xe-MRI (single breath-hold) were acquired on the same day, matched by their ventrodorsal position and coregistered for evaluation. Ventilation defect percentage (VDP) was calculated based on regional ventilation (RV), regional flow volume loops (RFVL), or ¹²⁹ Xe-MRI with two different threshold methods. A combined VDP was calculated for RV and RFVL. Additionally, lung function testing was performed (such as the forced expiratory volume in 1 second [FEV₁ ]) was used.

STATISTICAL TESTS

The obtained parameters were compared using Wilcoxon tests, correlated using Spearman's correlation coefficient (r), and agreement between PREFUL and ¹²⁹ Xe-MRI parameters was assessed using Bland-Altman analysis and Dice coefficients.

RESULTS

VDP measured by PREFUL and ¹²⁹ Xe were significantly correlated with both thresholding techniques (r = 0.62-0.69, P < 0.05 for all) and with lung function test parameters. Combined RV and RFVL PREFUL defect maps correlated with lung function testing (eg, with FEV₁ r = -0.87 P < 0.05), and showed better regional agreement to ¹²⁹ Xe-MRI ventilation defects (Dice coefficient defect 0.413) with significantly higher VDP values (10.2 ± 27.3, P = 0.04) than either PREFUL defect map alone.

DATA CONCLUSION

Combined RV and RFVL PREFUL defect maps likely increase sensitivity to mild airway obstruction with increased VDP values compared to ¹²⁹ Xe-MRI, and correlate strongly with lung function test parameters.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Lobar Ventilation in Patients with COPD Assessed with the Full-Scale Airway Network Flow Model and Xenon-enhanced Dual-Energy CT

Background The full-scale airway network (FAN) flow model shows excellent agreement with limited functional imaging data but requires further validation prior to clinical use. Purpose To validate the ventilation distributions computed with the FAN flow model with xenon ventilation from xenon-enhanced dual-energy (DE) CT in participants with chronic obstructive pulmonary disease (COPD). Materials and Methods In this prospective study, the FAN model extracted structural data from xenon-enhanced DE CT images of men with COPD scanned between June 2012 and July 2013 to compute gas ventilation dynamics. The ventilation distributions on the middle cross-section plane, percentage lobar ventilation, and ventilation heterogeneity quantified by the coefficient of variation (CV) were compared between xenon-enhanced DE CT imaging and the FAN model. The relationship between the ventilation parameters with the densitometry and pulmonary function test results was demonstrated. The agreements and correlations between the parameters were measured using the concordance correlation coefficient and the Pearson correlation coefficient. Results Twenty-two men with COPD (mean age, 67 years ± 7 [standard deviation]) were evaluated. The percentage lobar ventilation computed with FAN showed a strong positive correlation with xenon-enhanced DE CT data (r = 0.7, P < .001). Ninety-five percent of lobar ventilation CV differences lay within 95% confidence intervals. Correlations of the percentage lobar ventilation were negative for percentage emphysema (xenon-enhanced DE CT: r = -0.38, P < .001; FAN: r = -0.23, P = .02) but were positive for percentage normal tissue volume (xenon-enhanced DE CT: r = 0.78, P < .001; FAN: r = 0.45, P < .001). Lung CVs of FAN revealed negative correlations with the spirometry results (CV_FAN vs percentage predicted forced expiratory volume in 1 second: r = -0.75, P < .001; CV_FAN vs ratio of forced expiratory volume in 1 second to forced vital capacity: r = -0.67, P < .001). Conclusion The full-scale airway network modeled lobar ventilation in patients with chronic obstructive pulmonary disease correlated with the xenon-enhanced dual-energy CT imaging data. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Parraga and Eddy in this issue.

Anterior Pulmonary Ventilation Abnormalities in COVID-19

Online supplemental material is available for this article.