In this issue of Academic Radiology , Washko and colleagues have newly focused on voxel-based tissue volume (lung mass) rather than regional air content within a large cohort of chronic obstructive pulmonary disease (COPD) subjects studied in the context of the NIH-sponsored COPDGene project. After cleaning the data, 8156 subjects were available for baseline lung mass assessment using quantitative computed tomography (QCT) of full inspiratory chest scans. Of these subjects imaged at baseline, 1623 had the availability of a matched set of spirometrically derived data at both baseline and a 5-year return visit. When compared across a GOLD 0–4 spectrum, the study found that lung mass was greatest within the GOLD 1 group and then dropped off across GOLD 2–4, with a more rapid decline between GOLD 3 and 4. As subjects’ average height was nearly identical across all groups, lung volume variations were accounted for when looking at differences in lung mass without correction for subject size differences because of the very large number of subjects in this cohort. When modeling the baseline relationships as a means of predicting progression of airway obstruction (forced expiratory volume in 1 second [FEV 1 ]), the lung mass term was strongly predictive of FEV 1 decline. The model took into account subject height and weight, scanner make and model, and a number of other variables. This observation has brought a new focus for CT investigation. In particular, it brings into focus the fact that the underlying pathology associated with emphysema progression is not a simple unimodal increase in low attenuation area within the lung parenchyma. In the work of Washko and colleagues , they readily recognize the fact that at least two (and likely more) processes are occurring at the same time, with opposite effects on CT density. Thus, one must look at multiple CT metrics to provide an accurate reflection of the underlying pathophysiology. Because of this new focus on voxel-based tissue rather than air content, it is worth looking at the real and artifactual influences on the tissue metric.
From the earliest days of QCT, it has been recognized that the Hounsfield unit (HU) of a voxel within the lung field provides the percent air and percent tissue content of that voxel so long as imaging has been achieved without the use of a contrast agent and with the assumption that a voxel comprised air and “tissue” . “Tissue” comprises all non-air components, including (but not limited to) airway walls, alveolar structure, alveolar and interstitial fluid accumulation, mucous, blood volume (venous, capillary, and arterial), and blood vessel walls. Longitudinal and cross-sectional comparisons can certainly provide indices of anatomic and physiological changes in the lung well beyond an index of air trapping and emphysematous destruction, indices commonly used as QCT-associated metrics in the study of COPD. Conversely, because of the complex nature of the underlying structure and pathologies—including the presence of inflammation, alterations in blood flow, and tissue remodeling—the changes in regional air and tissue volumes are complex. Thus, when multiple processes are going on at the same time, one process can dominate the others when a single voxel-based assessment is used to seek an understanding of everything simultaneously. By placing percent tissue volume (otherwise referred to as lung mass) in context with clinically derived metrics, the measure of lung mass (both longitudinal and in a cross-sectional study design) can begin to provide new insights into the COPD lung. However, the implications of altered voxel “tissue” content must be considered in the context of a differential assessment of underlying pathophysiology.
In addition to the actual underlying pathophysiological influences on regional tissue metrics, there are other influences to consider. Despite the intention that an HU be standardized across all manufacturers, scanner models, and reconstruction methods, there has been variability in the intrathoracic air and tissue values, more so for air than tissue. This was recognized by Washko et al. through their inclusion of scanner make and model when modeling the relationship between baseline lung mass and FEV 1 decline. Multicenter studies have served to bring about an attempt at harmonization of the QCT-derived metrics used in an assessment of regional tissue and air volumes in the lung, and these standardized approaches that adjust the protocols across scanner make and models were outlined recently by Sieren et al. . Histogram distributions for values of air, in an online supplement to the article, have been shown to vary across scanners and scanner models and have been shown to be accentuated depending upon the image reconstruction kernel. Recent efforts to better correct for scatter artifacts, beam hardening, and noise have served to bring air and tissue values closer to the targeted values of −1000 HU and ~65 HU, respectively .
Sieren et al. , reporting on an early set of 952 subjects with total lung capacity (TLC) and residual volume (RV) scans obtained at baseline and 1 year in SPIROMICS, have demonstrated that, if scanner-adjusted imaging and reconstruction protocols are followed, lung tissue volume (right panels of Fig 1 ) correlations across a 1-year interval are tight. This is despite greater scatter in the lung air content values (left panels of Fig 1 ) associated with differences in subject efforts to achieve TLC and RV. The greater reliability of tissue compared to air content of lung voxels is the result of tissue content remaining fairly constant across lung volumes, with the exception of a small shift in regional blood volumes with lung inflation.
Open full size image
Figure 1
One-year repeatability of lung air (left panels) and tissue volumes (right panels) at total lung capacity (upper panels) and residual volume (lower panels). These data are from 952 subjects studied within the SPIROMICS cohort. Modified from Sieren et al. .
As previously demonstrated ,
total tissue volume in a voxel=voxel volume×(HU−CTair/CTtissue−CTair) total tissue volume in a voxel
=
voxel volume
×
(
HU
−
CTair
/
CTtissue
−
CTair
)
where CTair = −1000 HU and CTtissue = 65 HU.
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
References
1. Washko G.R., Kinney G.L., Ross J.C., et. al.: Lung mass in smokers. Academic Radiol 2017;
2. Hoffman E.A.: Effect of body orientation on regional lung expansion: a computed tomographic approach. J Appl Physiol 1985; 59: pp. 468-480.
3. Coxson H.O., Mayo J.R., Behzad H., et. al.: Measurement of lung expansion with computed tomography and comparison with quantitative histology. J Appl Physiol 1995; 79: pp. 1525-1530.
4. Sieren J.P., Newell J.D., Barr R.G., et. al.: Protocol for multicenter quantitative computed tomography to phenotype the lungs. Am J Respir Crit Care Med 2016; 194: pp. 794-806.
5. Mobberley S.D., Fuld M.K., Sieren J.P., et. al.: Scatter correction associated with dedicated dual-source CT hardware improves accuracy of lung air measures. Acad Radiol 2013; 20: pp. 1334-1343.
6. Sieren J.P., Hoffman E.A., Fuld M.K., et. al.: Sinogram Affirmed Iterative Reconstruction (SAFIRE) versus weighted filtered back projection (WFBP) effects on quantitative measure in the COPDGene 2 test object. Med Phys 2014; 41: pp. 091910.
7. Newell J.D., Fuld M.K., Allmendinger T., et. al.: Very low-dose (0.15 mGy) chest CT protocols using the COPDGene 2 test object and a third-generation dual-source CT scanner with corresponding third-generation iterative reconstruction software. Invest Radiol 2015; 50: pp. 40-45.
8. Yin Y., Hoffman E.A., Ding K., et. al.: A cubic B-spline-based hybrid registration of lung CT images for a dynamic airway geometric model with large deformation. Phys Med Biol 2011; 56: pp. 203-218.
9. Ding K., Cao K., Fuld M.K., et. al.: Comparison of image registration based measures of regional lung ventilation from dynamic spiral CT with Xe-CT. Med Phys 2012; 39: pp. 5084-5098.
10. Galbán C.J., Han M.K., Boes J.L., et. al.: Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression. Nat Med 2012; 18: pp. 1711-1715.
11. Boes J.L., Hoff B.A., Bule M., et. al.: Parametric response mapping monitors temporal changes on lung CT scans in the subpopulations and intermediate outcome measures in COPD Study (SPIROMICS). Acad Radiol 2015; 22: pp. 186-194.
12. Bhatt S.P., Soler X., Wang X., et. al.: Association between functional small airway disease and FEV1 decline in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2016; 194: pp. 178-184.
13. McDonough J.E., Yuan R., Suzuki M., et. al.: Small airway obstruction and emphysema in chronic obstructive pulmonary disease. N Engl J Med 2011; 365: pp. 1567-1575.
14. Smith B.M., Hoffman E.A., Rabinowitz D., et. al.: Comparison of spatially matched airways reveals thinner airway walls in COPD. The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study and the Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS). Thorax 2014; 69: pp. 987-996.
15. Tanabe N., Vasilescu D.M., McDonough J.E., et. al.: MicroCT comparison of preterminal bronchioles in centrilobular and panlobular emphysema. Am J Respir Crit Care Med 2016; Sep 9; Epub ahead of print
16. Iyer K.S., Newell J.D., Jin D., et. al.: Quantitative dual-energy computed tomography supports a vascular etiology of smoking-induced inflammatory lung disease. Am J Respir Crit Care Med 2016; 193: pp. 652-661.
17. Hueper K., Vogel-Claussen J., Parikh M.A., et. al.: Pulmonary microvascular blood flow in mild chronic obstructive pulmonary disease and emphysema. The MESA COPD Study. Am J Respir Crit Care Med 2015; 192: pp. 570-580.
18. Fuld M.K., Halaweish A.F., Newell J.D., et. al.: Optimization of dual-energy xenon-computed tomography for quantitative assessment of regional pulmonary ventilation. Invest Radiol 2013; 48: pp. 629-637.
19. Hwang H.J., Hoffman E.A., Lee C.H., et. al.: The role of dual-energy computed tomography in the assessment of pulmonary function. Eur J Radiol 2017; 86: pp. 320-334.
20. Yablonskiy D.A., Sukstanskii A.L., Leawoods J.C., et. al.: Quantitative in vivo assessment of lung microstructure at the alveolar level with hyperpolarized 3He diffusion MRI. Proc Natl Acad Sci USA 2002; 99: pp. 3111-3116.
21. Thomen R.P., Quirk J.D., Roach D., et. al.: Direct comparison of (129) Xe diffusion measurements with quantitative histology in human lungs. Magn Reson Med 2017; 77: pp. 265-272.