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

Rapid pulmonary 129Xe ventilation MRI of discharged COVID-19 patients with zigzag sampling

PURPOSE

To demonstrate the feasibility of zigzag sampling for 3D rapid hyperpolarized 129Xe ventilation MRI in human.

METHODS

Zigzag sampling in one direction was combined with gradient-recalled echo sequence (GRE-zigzag-Y) to acquire hyperpolarized 129Xe ventilation images. Image quality was compared with a balanced SSFP (bSSFP) sequence with the same spatial resolution for 12 healthy volunteers (HVs). For another 8 HVs and 9 discharged coronavirus disease 2019 subjects, isotropic resolution 129Xe ventilation images were acquired using zigzag sampling in two directions through GRE-zigzag-YZ. 129Xe ventilation defect percent (VDP) was quantified for GRE-zigzag-YZ and bSSFP acquisitions. Relationships and agreement between these VDP measurements were evaluated using Pearson correlation coefficient (r) and Bland-Altman analysis.

RESULTS

For 12 HVs, GRE-zigzag-Y and bSSFP required 2.2 s and 10.5 s, respectively, to acquire 129Xe images with a spatial resolution of 3.96 × 3.96 × 10.5 mm3. Structural similarity index, mean absolute error, and Dice similarity coefficient between the two sets of images and ventilated lung regions were 0.85 ± 0.03, 0.0015 ± 0.0001, and 0.91 ± 0.02, respectively. For another 8 HVs and 9 coronavirus disease 2019 subjects, 129Xe images with a nominal spatial resolution of 2.5 × 2.5 × 2.5 mm3 were acquired within 5.5 s per subject using GRE-zigzag-YZ. VDP provided by GRE-zigzag-YZ was strongly correlated (R2 = 0.93, p < 0.0001) with that generated by bSSFP with minimal biases (bias = -0.005%, 95% limit-of-agreement = [-0.414%, 0.424%]).

CONCLUSION

Zigzag sampling combined with GRE sequence provides a way for rapid 129Xe ventilation imaging.

ERS International Congress 2023: highlights from the Respiratory Clinical Care and Physiology Assembly

It is a challenge to keep abreast of all the clinical and scientific advances in the field of respiratory medicine. This article contains an overview of laboratory-based science, clinical trials and qualitative research that were presented during the 2023 European Respiratory Society International Congress within the sessions from the five groups of Assembly 1 (Respiratory Clinical Care and Physiology). Selected presentations are summarised from a wide range of topics: clinical problems, rehabilitation and chronic care, general practice and primary care, electronic/mobile health (e-health/m-health), clinical respiratory physiology, exercise and functional imaging.

Functional imaging for assessing regional lung ventilation in preclinical and clinical research

Dynamic heterogeneity in lung ventilation is an important measure of pulmonary function and may be characteristic of early pulmonary disease. While standard indices like spirometry, body plethysmography, and blood gases have been utilized to assess lung function, they do not provide adequate information on regional ventilatory distribution nor function assessments of ventilation during the respiratory cycle. Emerging technologies such as xenon CT, volumetric CT, functional MRI and X-ray velocimetry can assess regional ventilation using non-invasive radiographic methods that may complement current methods of assessing lung function. As a supplement to current modalities of pulmonary function assessment, functional lung imaging has the potential to identify respiratory disease phenotypes with distinct natural histories. Moreover, these novel technologies may offer an optimal strategy to evaluate the effectiveness of novel therapies and therapies targeting localized small airways disease in preclinical and clinical research. In this review, we aim to discuss the features of functional lung imaging, as well as its potential application and limitations to adoption in research.

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.

Deep Learning-Based Computed Tomography Features in Evaluating Early Screening and Risk Factors for Chronic Obstructive Pulmonary Disease

This research aimed to investigate the diagnostic effect of computed tomography (CT) images based on a deep learning double residual convolution neural network (DRCNN) model on chronic obstructive pulmonary disease (COPD) and the related risk factors for COPD. The questionnaire survey was conducted among 980 permanent residents aged ≥ 40 years old. Among them, 84 patients who were diagnosed with COPD and volunteered to participate in the experiment and 25 healthy people were selected as the research subjects, and all of them underwent CT imaging scans. At the same time, an image noise reduction model based on the DRCNN was proposed to process CT images. The results showed that 84 of 980 subjects were diagnosed with COPD, and the overall prevalence of COPD in this epidemiological survey was 8.57%. Multivariate logistic regression model analysis showed that the regression coefficients of COPD with age, family history of COPD, and smoking were 0.557, 0.513, and 0.717, respectively (P < 0.05). The diagnostic sensitivity, specificity, and accuracy of DRCNN-based CT for COPD were greatly superior to those of single CT and the difference was considerable (P < 0.05). In summary, advanced age, family history of COPD, and smoking were independent risk factors for COPD. CT based on the DRCNN model can improve the diagnostic accuracy of simple CT images for COPD and has good performance in the early screening of COPD.

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.