Lung density on high resolution computer tomography (HRCT) reflects degree of inflammation in smokers

Lung density changes with growth and inflation

BACKGROUND

With body growth from childhood, the lungs can enlarge by either increasing the volume of air in the periphery (as would occur with inspiration) or by increasing the number of peripheral acinar units. In the former case, the lung tissue density would decrease with inflation, whereas in the latter case, the lung density would be relatively constant as the lung grows. To address this fundamental structural issue, we measured the CT scan density in human subjects of widely varying size at two different lung volumes.

METHODS

Five hundred one subjects were enrolled in the study. They underwent a chest CT scan at full inspiration and another scan at end expiration. Spirometry, body plethysmography, and diffusing capacity of the lung for carbon monoxide were also measured.

RESULTS

There was a strong correlation between the size of the lungs measured at full inspiration on CT scan and the mean lung density (r = -0.72, P = .001). People with larger lungs had significantly lower mean lung density. These density changes among different subjects overlapped the density changes within subjects at different lung volumes.

CONCLUSIONS

Lung structure in subjects with larger lungs is different from that in subjects with smaller lungs. Tissue volume does not increase in proportion to lung size, as would be required if larger lungs just had more alveoli. These observations suggest that the growth of the lung into adulthood is not accompanied by new alveoli, but rather by enlargement of existing structures. The presence of greater air spaces in larger lungs could impact the occurrence and pathogenesis of spontaneous pneumothorax or COPD.

Quantative computerized tomography assessment of lung density as a predictor of postoperative pulmonary morbidity in patients with lung cancer

BACKGROUND

The aim of this study was to evaluate the pulmonary reserve of the patients via preoperative quantitative computerized tomography (CT) and to determine if these preoperative quantitative measurements could predict the postoperative pulmonary morbidity.

METHODS

Fifty patients with lung cancer who underwent lobectomy/segmentectomy were included in the study. Preoperative quantitative CT scans and pulmonary function tests data were evaluated retrospectively. We compare these measurements with postoperative morbidity.

RESULTS

There were 32 males and 18 females with a mean age of 54.4±13.9 years. Mean total density was -790.6±73.4 HU. The volume of emphysematous lung was (<-900 HU) 885.2±1,378.4 cm(3). Forced expiratory volume in one second (FEV1) (r=-0.494, P=0.02) and diffusion capacity of carbon monoxide (DLCO) (r=-0.643, P<0.001) were found to be correlate with the volume of emphysematous lung. Furthermore FEV1 (r=0.59, P<0.001) and DLCO (r=0.48, P<0.001) were also found to be correlate with mean lung density. Postoperative pulmonary morbidity was significantly higher in patients with lower lung density (P<0.001), larger volume of emphysema (P<0.001) and lower DLCO (P=0.039). A cut-off point of -787.5 HU for lung density showed 86.96% sensitivity and 81.48% specificity for predicting the pulmonary morbidity (kappa =-0.68, P<0.001). Additionally a cut-off point of 5.41% for emphysematous volume showed 84.00% sensitivity and 80.00% specificity for predicting the pulmonary morbidity (kappa =0.64, P<0.001). According to logistic regression analyses emphysematous volume >5.41% (P=0.014) and lung density <-787.5 HU (P=0.009) were independent prognostic factors associated with postoperative pulmonary morbidity.

CONCLUSIONS

In this study, the patients with a lower lung density than -787.5 HU and a higher volume of emphysema than 5.41% were found to be at increased risk for developing postoperative pulmonary morbidity. More stringent precautions should be taken in those patients that were found to be at high risk to avoid pulmonary complications.

Emission-based estimation of lung attenuation coefficients for attenuation correction in time-of-flight PET/MR

In standard segmentation-based MRI-guided attenuation correction (MRAC) of PET data on hybrid PET/MRI systems, the inter/intra-patient variability of linear attenuation coefficients (LACs) is ignored owing to the assignment of a constant LAC to each tissue class. This can lead to PET quantification errors, especially in the lung regions. In this work, we aim to derive continuous and patient-specific lung LACs from time-of-flight (TOF) PET emission data using the maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm. The MLAA algorithm was constrained for estimation of lung LACs only in the standard 4-class MR attenuation map using Gaussian lung tissue preference and Markov random field smoothness priors. MRAC maps were derived from segmentation of CT images of 19 TOF-PET/CT clinical studies into background air, lung, soft tissue and fat tissue classes, followed by assignment of predefined LACs of 0, 0.0224, 0.0864 and 0.0975 cm(-1), respectively. The lung LACs of the resulting attenuation maps were then estimated from emission data using the proposed MLAA algorithm. PET quantification accuracy of MRAC and MLAA methods was evaluated against the reference CT-based AC method in the lungs, lesions located in/near the lungs and neighbouring tissues. The results show that the proposed MLAA algorithm is capable of retrieving lung density gradients and compensate fairly for respiratory-phase mismatch between PET and corresponding attenuation maps. It was found that the mean of the estimated lung LACs generally follow the trend of the reference CT-based attenuation correction (CTAC) method. Quantitative analysis revealed that the MRAC method resulted in average relative errors of -5.2 ± 7.1% and -6.1 ± 6.7% in the lungs and lesions, respectively. These were reduced by the MLAA algorithm to -0.8 ± 6.3% and -3.3 ± 4.7%, respectively. In conclusion, we demonstrated the potential and capability of emission-based methods in deriving patient-specific lung LACs to improve the accuracy of attenuation correction in TOF PET/MR imaging, thus paving the way for their adaptation in the clinic.

Chronic Obstructive Pulmonary Disease: Pulmonary Function and CT Lung Attenuation Do Not Show Linear Correlation

PURPOSE

To determine whether the relationship between pulmonary function and computed tomographic (CT) lung attenuation in chronic obstructive pulmonary disease (COPD), which is traditionally described with single univariate and multivariate statistical models, could be more accurately described with a multiple model estimation approach.

MATERIALS AND METHODS

The study was approved by the local ethics committee. All participants provided written informed consent. The prediction of the percentage area with CT attenuation values less than -950 HU at inspiration (%LAA-950insp) and less than -910 HU at expiration (%LAA-910exp) obtained with single univariate and multivariate models was compared with that obtained with a multiple model estimation approach in 132 patients with COPD.

RESULTS

At univariate analysis, %LAA-950insp and %LAA-910exp values higher than the mean value of this cohort (19.1% and 22.0%) showed better correlation with percentage of predicted diffusing capacity of lung for carbon monoxide (Dlco%) than with airflow obstruction (forced expiratory volume in 1 second [FEV1]/vital capacity [VC]). Conversely, %LAA-950insp and %LAA-910exp values lower than the mean value were correlated with FEV1/VC but not with Dlco%. Multiple model estimation performed with two multivariate regressions, each selecting the most appropriate functional variables (FEV1/VC for mild parenchymal destruction, Dlco% and functional residual capacity for severe parenchymal destruction), predicted better than single multivariate regression both %LAA-950insp (R(2) = 0.75 vs 0.46) and %LAA-910exp (R(2) = 0.83 vs 0.63).

CONCLUSION

The relationship between pulmonary function data and CT densitometric changes in COPD varies with the level of lung attenuation impairment. The nonlinear profile of this relationship is accurately predicted with a multiple model estimation approach.