Influence of Inspiratory/Expiratory CT Registration on Quantitative Air Trapping

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.

State-of-the-art review of lung imaging in cystic fibrosis with recommendations for pulmonologists and radiologists from the "iMAging managEment of cySTic fibROsis" (MAESTRO) consortium

OBJECTIVE

Imaging represents an important noninvasive means to assess cystic fibrosis (CF) lung disease, which remains the main cause of morbidity and mortality in CF patients. While the development of new imaging techniques has revolutionised clinical practice, advances have posed diagnostic and monitoring challenges. The authors aim to summarise these challenges and make evidence-based recommendations regarding imaging assessment for both clinicians and radiologists.

STUDY DESIGN

A committee of 21 experts in CF from the 10 largest specialist centres in Italy was convened, including a radiologist and a pulmonologist from each centre, with the overall aim of developing clear and actionable recommendations for lung imaging in CF. An a priori threshold of at least 80% of the votes was required for acceptance of each statement of recommendation.

RESULTS

After a systematic review of the relevant literature, the committee convened to evaluate 167 articles. Following five RAND conferences, consensus statements were developed by an executive subcommittee. The entire consensus committee voted and approved 28 main statements.

CONCLUSIONS

There is a need for international guidelines regarding the appropriate timing and selection of imaging modality for patients with CF lung disease; timing and selection depends upon the clinical scenario, the patient's age, lung function and type of treatment. Despite its ubiquity, the use of the chest radiograph remains controversial. Both computed tomography and magnetic resonance imaging should be routinely used to monitor CF lung disease. Future studies should focus on imaging protocol harmonisation both for computed tomography and for magnetic resonance imaging. The introduction of artificial intelligence imaging analysis may further revolutionise clinical practice by providing fast and reliable quantitative outcomes to assess disease status. To date, there is no evidence supporting the use of lung ultrasound to monitor CF lung disease.

Computed tomography-identified phenotypes of small airway obstructions in chronic obstructive pulmonary disease

ABSTRACT

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characteristic of small airway inflammation, obstruction, and emphysema. It is well known that spirometry alone cannot differentiate each separate component. Computed tomography (CT) is widely used to determine the extent of emphysema and small airway involvement in COPD. Compared with the pulmonary function test, small airway CT phenotypes can accurately reflect disease severity in patients with COPD, which is conducive to improving the prognosis of this disease. CT measurement of central airway morphology has been applied in clinical, epidemiologic, and genetic investigations as an inference of the presence and severity of small airway disease. This review will focus on presenting the current knowledge and methodologies in chest CT that aid in identifying discrete COPD phenotypes.

Improved detection of air trapping on expiratory computed tomography using deep learning

BACKGROUND

Radiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression.

OBJECTIVE

To investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques.

MATERIALS AND METHODS

Paired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model.

RESULTS

QAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach.

CONCLUSION

The CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients.

Prospective longitudinal association between repeated multiple breath washout measurements and computed tomography scores in children with cystic fibrosis

BACKGROUND

Progression of structural lung disease (SLD) is a major risk factor for morbidity in patients with cystic fibrosis (CF). We studied changes in SLD and correlations with spirometry and nitrogen multiple breath washout (N₂MBW) outcomes to explore associations in contemporary evolution between structural and functional abnormalities in CF lung disease.

METHODS

Spirometry-controlled chest-CTs using PRAGMA-CF for scoring extent of SLD, spirometry, and N₂MBW were performed at two-year intervals in school-age children with CF.

RESULTS

Fifty-seven children aged 6-18 years were included. No significant progression in mean PRAGMA-CF scores was observed. Half of the children showed improvement in the proportion of bronchiectasis (%Bx). Lung Clearance Index (LCI) and the second moment ratio (M2) increased significantly and baseline values correlated significantly with SLD at follow-up (p ≤ 0.0002). The correlation between the change in M2 (∆M2) and the change in total SLD was R = 0.27 (p = 0.048). We found high negative predictive values (100%) for ∆M2<10% to exclude progression in SLD. For stable or improving values of LCI and M2, the predicted probability for progression in SLD was 16% and 14%, respectively (upper 95% confidence limit: 33%). Evolution in N₂MBW and CT outcomes was discordant in half of the children.

CONCLUSIONS

We found no progression in SLD over 2 years in school-age children with CF, in contrast to both LCI and M2, which along with discordant outcomes in half of the children underlines that N₂MBW and CT assess different aspects of CF lung disease. However, stable outcomes from N₂MBW were associated with stable structural lung disease.

[Cystic fibrosis and computed tomography of the lungs]

With its high detail of morphological changes in lung parenchyma and airways as well as the possibilities for three-dimensional reconstruction, computed tomography (CT) represents a solid tool for the diagnosis and follow-up in patients suffering from cystic fibrosis (CF). Guidelines for standardized CT image acquisition in CF patients are still missing. In the mostly younger CF patients, an important issue is the well-considered use of radiation in CT imaging. The use of intravenous contrast agent is mainly restricted to acute emergency diagnostics. Typical morphological findings in CF lung disease are bronchiectasis, mucus plugging, or signs of decreased ventilation (air trapping) which can be detected with CT even in early stages. Various scoring systems that have become established over time are used to grade disease severity and for structured follow-up, e.g., in clinical research studies. With the technical development of CT, a number of postprocessing software tools were developed to help clinical reporting and overcome interreader differences for a standardized quantification. As an imaging modality free of ionizing radiation, magnetic resonance imaging (MRI) is becoming increasingly important in the diagnosis and follow-up of CF patients and is already frequently a substitute for CT for long-term follow-up at numerous specialized centers.

Mucus plugging, air trapping, and bronchiectasis are important outcome measures in assessing progressive childhood cystic fibrosis lung disease

OBJECTIVE

To determine which outcome measures could detect early progression of disease in school-age children with mild cystic fibrosis (CF) lung disease over a two-year time interval utilizing chest computed tomography (CT) scores, quantitative CT air trapping (QAT), and spirometric measurements.

METHODS

Thirty-six school-age children with mild CF lung disease (median [interquartile range] age 12 [3.7] years; percent predicted forced expiratory volume in 1 second (ppFEV₁ ) 99 [12.5]) were evaluated by serial spirometer-controlled chest CT scans and spirometry at baseline, 3-month, 1- and 2-years.

RESULTS

No significant changes were noted at 3-month for any variable except for decreased ppFEV₁ . Mucus plugging score (MPS) and QAT_A1andA2 increased at 1- and 2-years. The bronchiectasis score (BS), and total score (TS) were increased at 2-year. All variables tested with the exception of bronchial wall thickness score, parenchymal score (PS), and ppFEV₁ , were consistent with longitudinal worsening of lung disease. Multivariate analysis revealed baseline PS, baseline TS, and 1-year changes in BS and air trapping score were predictive of 2-year changes in BS.

CONCLUSIONS

MPS and QAT_A1-A2 were the most sensitive indicators of progressive childhood CF lung disease. The 1-year change in the bronchiectasis score had the most positive predictive power for 2-year change in bronchiectasis.