CNN-based Deformable Registration Facilitates Fast and Accurate Air Trapping Measurements at Inspiratory and Expiratory CT

Reference formulas for chest CT-derived lobar volumes in the lung-healthy general population

INTRODUCTION

Lung hyperinflation, a key contributor to dyspnea in chronic obstructive pulmonary disease (COPD), can be quantified via chest computed tomography (CT). Establishing reference equations for lobar volumes and total lung volume (TLV) can aid in evaluating lobar hyperinflation, especially for targeted lung volume reduction therapies.

METHODS

The Imaging in Lifelines study (ImaLife) comprises 11,729 participants aged 45 and above with analyzed inspiratory low-dose thoracic CT scans. Lung and lobar volumes were measured using an automatic AI-based segmentation algorithm (LungSeg). For the main analysis, participants were excluded if they had self-reported COPD/asthma, lung disease on CT, airflow obstruction on lung function testing, were currently smoking, aged over 80 years, or had height outside the 99% confidence interval. Reference equations for TLV and lobar volumes were determined using linear regression considering age and height, stratified by sex. For the subanalysis, participants who were currently smoking or experiencing airflow obstruction were compared to the group of the main analysis.

RESULTS

The study included 7306 lung-healthy participants, 97.5% Caucasian, 43.6% men, with mean age of 60.3 ± 9.5 years. Lung and lobar volumes generally increased with age and height. Men consistently had higher volumes than women when adjusted for height. R2 values ranged from 7.8 to 19.9%. In smokers and those with airway obstruction, volumes were larger than in lung-healthy groups, with the largest increases measured in the upper lobes.

CONCLUSION

The established reference equations for CT-derived TLV and lobar volumes provide a standardized interpretation for individuals aged 45 to 80 of Northern European descent.

KEY POINTS

Question Lobar lung volumes can be derived from inspiratory CT scans, but healthy-lung reference values are lacking. Findings Lung and lobar volumes generally increased with age and height. Reference equations for lung/lobar volumes were derived from a sizeable lung-healthy population. Clinical relevance This study provides reference equations for inspiratory CT-derived lung and lobar volumes in a lung-healthy population, potentially useful for assessing candidates for lung volume reduction therapies, for lobe removal in lung cancer patients, and in case of restrictive pulmonary diseases.

Chronic Lung Injury after COVID-19 Pneumonia: Clinical, Radiologic, and Histopathologic Perspectives

With the COVID-19 pandemic having lasted more than 3 years, concerns are growing about prolonged symptoms and respiratory complications in COVID-19 survivors, collectively termed post-COVID-19 condition (PCC). Up to 50% of patients have residual symptoms and physiologic impairment, particularly dyspnea and reduced diffusion capacity. Studies have also shown that 24%-54% of patients hospitalized during the 1st year of the pandemic exhibit radiologic abnormalities, such as ground-glass opacity, reticular opacity, bronchial dilatation, and air trapping, when imaged more than 1 year after infection. In patients with persistent respiratory symptoms but normal results at chest CT, dual-energy contrast-enhanced CT, xenon 129 MRI, and low-field-strength MRI were reported to show abnormal ventilation and/or perfusion, suggesting that some lung injury may not be detectable with standard CT. Histologic patterns in post-COVID-19 lung disease include fibrosis, organizing pneumonia, and vascular abnormality, indicating that different pathologic mechanisms may contribute to PCC. Therefore, a comprehensive imaging approach is necessary to evaluate and diagnose patients with persistent post-COVID-19 symptoms. This review will focus on the long-term findings of clinical and radiologic abnormalities and describe histopathologic perspectives. It also addresses advanced imaging techniques and deep learning approaches that can be applied to COVID-19 survivors. This field remains an active area of research, and further follow-up studies are warranted for a better understanding of the chronic stage of the disease and developing a multidisciplinary approach for patient management.

Computer-assisted evaluation of small airway disease in CT scans of Iran-Iraq war victims of chemical warfare by a locally developed software: comparison between different quantitative methods

OBJECTIVE

Diagnosis of small airway disease on computed tomography (CT) scans is challenging in patients with a history of chemical warfare exposure. We developed a software package based on different methodologies to identify and quantify small airway disease in CT images. The primary aim was to identify the best automatic methodology for detecting small airway disease in CT scans of Iran-Iraq War victims of chemical warfare.

METHODS

This retrospective case-control study enrolled 46 patients with a history of chemical warfare exposure and 27 controls with inspiratory/expiratory (I/E) CT scans and spirometry tests. Image data were automatically segmented, and inspiratory images were registered into the expiratory images' frame using the locally developed software. Parametric response mapping (PRM) and air trapping index (ATI) mapping were performed on the CT images. Conventional QCT methods, including expiratory/inspiratory mean lung attenuation (E/I MLA) ratio, normal density E/I (ND E/I) MLA ratio, attenuation volume Index (AVI), %low attenuation areas (LAA) < -856 in exhale scans, and %LAA < -950 in inhale scans were also computed. QCT measurements were correlated with spirometry results and compared across the two study groups.

RESULTS

The correlation analysis showed a significant negative relationship between three air trapping (AT) measurements (PRM, ATI, and %LAAExp < -856) and spirometry parameters (Fev1, Fvc, Fev1/Fvc, and MMEF). Moreover, %LAAExp < -856 had the highest significant negative correlation with Fev1/Fvc (r = -0.643, P-value < 0.001). Three AT measurements demonstrated a significant difference between the study groups. The E/I ratio was also significantly different between the two groups (P-value < 0.001). Binary logistic regression models showed PRMFsad, %LAAExp < -856, and ATI as significant and strong predictors of the study outcome. Optimal cut-points for PRMFsad = 19%, %LAAExp < -856 = 23%, and ATI = 27% were identified to classify the participants into two groups with high accuracy.

CONCLUSION

QCT methods, including PRM, ATI, and %LAAExp < -856 can greatly advance the identification and quantification of SAD in chemical warfare victims. The results should be verified in well-designed prospective studies involving a large population.

A Computer-based Analysis for Identification and Quantification of Small Airway Disease in Lung Computed Tomography Images: A Comprehensive Review for Radiologists

Computed tomography (CT) imaging is being increasingly used in clinical practice for detailed characterization of lung diseases. Respiratory diseases involve various components of the lung, including the small airways. Evaluation of small airway disease on CT images is challenging as the airways cannot be visualized directly by a CT scanner. Small airway disease can manifest as pulmonary air trapping (AT). Although AT may be sometimes seen as mosaic attenuation on expiratory CT images, it is difficult to identify diffuse AT visually. Computer technology advances over the past decades have provided methods for objective quantification of small airway disease on CT images. Quantitative CT (QCT) methods are being rapidly developed to quantify underlying lung diseases with greater precision than subjective visual assessment of CT images. A growing body of evidence suggests that QCT methods can be practical tools in the clinical setting to identify and quantify abnormal regions of the lung accurately and reproducibly. This review aimed to describe the available methods for the identification and quantification of small airway disease on CT images and to discuss the challenges of implementing QCT metrics in clinical care for patients with small airway disease.