Despite the challenges, cost, and widespread prevalence of chronic obstructive pulmonary disease (COPD) , pulmonary imaging currently plays a limited role in staging and guiding therapy. However, recent advances in quantitative imaging measures of structural and functional changes argue for a more central role for both computed tomography and magnetic resonance imaging (MRI) in characterizing COPD phenotypes and severity .
Hyperpolarized (HP) gas MRI technology, in particular, has played a central role in confirming the importance of ventilation heterogeneity as a hallmark of obstructive lung disease , underscoring the need to understand regionally focused disease processes and patterns both in COPD and in asthma. Nonetheless, the hard work of quantifying and validating imaging measures of ventilation against established reference standards, such as pulmonary function tests, has slowed advancement. This is due mostly to the challenges of comparing a regional to a global measure and conducting large studies in well-characterized disease populations.
However, there are signs that translation of this technology is accelerating. In this issue, Davis et al. report a major advancement using HP gas MRI to characterize COPD severity. Importantly, they present the first study to compare quantitative measures of ventilation defect percentage (VDP) derived from HP gas MRI to an analogous measure of ventilation heterogeneity called the “poorly communicating fraction” (PCF), and they do so in a sufficiently large population of subjects, including older never-smokers (N = 45) and patients with various severities of COPD (N = 101), for meaningful interpretation. The VDP is measured from images of ventilation acquired during a single breath-hold of HP 3He gas. The nonventilated lung volume is summed and normalized to total thoracic cavity volume (TCV) segmented from a conventional (i.e. proton) MRI acquired at a breath-hold matched to the same lung inflation . Moreover, several automated methods for calculating VDP have now been developed, making analysis time efficient and highly repeatable .
The PCF is a measure of considerable interest because it has been shown independently to be a biomarker of COPD severity and decreased exercise capacity . The PCF is derived from alveolar volume (Va) measured using the single-breath tracer gas dilution technique that is paired with the carbon monoxide-diffusing capacity measure . The PCF is also normalized to the total lung capacity (TLC) using plethysmography. Because both VDP and PCF are measured using single-breath estimations during rest and are derived from an inert tracer gas with similar diffusivity (3He vs 10Ne), they might be expected to probe an equivalent subset of well-ventilated regions of the lungs. Furthermore, both measures are also normalized to lung inflation volume and are expressed as a percent.
The authors indeed found a high degree of correlation between VDP and PCF, and more importantly, a similar qualitative dependence on COPD severity. One caveat is that the VDP was biased toward lower values compared to PCF, especially in patients with COPD with more severe disease (Global Initiative for Chronic Obstructive Lung Disease [GOLD] stage III and IV). An important interpretation of these results is that the VDP measures have a strong basis in physiology, while also depicting the specific regions of the lung with impaired ventilation. Such regional information provides obvious advantages over global measures for improving the precision of inhaled and regionally delivered (e.g. bronchial stent ) therapies. Thus, the rigorous scientific approach of Davis et al. supports an improved physiologic interpretation of the VDP measure, while confirming an association of VDP with COPD severity.
Of course, important fundamental differences between the two measures exist, the most obvious difference being that the VDP is normalized to the TCV acquired at ~1 L above functional residual capacity (FRC) in the supine position, whereas the PCF is normalized to and acquired at TLC. The volume difference impacts the lung volume accessed by the two methods. FRC is known to be as much as 30% lower in the supine vs upright postures , and the recruitment of additional lung units at the increased positive pressure of TLC lung inflation is likely to differentially affect the PCF compared to VDP. On first impression, one might predict that PCF would be <VDP due to inflation volume differences. For example, airway closure may be progressive with lung deflation, and thus more airways will have reopened at TLC than at FRC+1 L. However, to the contrary, PCF is greater than VDP, especially for more severe COPD. Therefore, PCF is likely to be more sensitive to changes in lung compliance due to emphysema and hyperinflation resulting from airway-parenchymal uncoupling than VDP. It is interesting to note that the PCF has a nonlinear relationship with FEV1%p , especially for the most severe GOLD stages III and IV, further supporting the likelihood that PCF is amplified for those patients with the most severe airflow limitation. It is possible that this nonlinearity stems from the more severe emphysema typical of these more severe stages. Neder et al. specifically associates PCF with markers of exercise impairment, including end-exercise dyspnea and peak exercise capacity that reflect progressed emphysema. In fact, the PCF was shown to be a better predictor of reduced peak exercise capacity than any other conventional spirometry measures. The Va measurement used for PCF may also be difficult to interpret physiologically as a number of factors can influence the accessible lung volume. Besides poor mixing, restrictive lung disease and muscle weakness also play a major role in reducing the measured Va .
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. World Health Organization : Chronic respiratory diseases: burden of COPD. Available at: http://www.who.int/respiratory/copd/burden/en/
2. Washko G.R., Parraga G., Coxson H.O.: Quantitative pulmonary imaging using computed tomography and magnetic resonance imaging. Respirology 2012; 17: pp. 432-444.
3. Altes T.A., Powers P.L., Knight-Scott J., et. al.: Hyperpolarized 3He MR lung ventilation imaging in asthmatics: preliminary findings. J Magn Reson Imaging 2001; 13: pp. 378-384.
4. Samee S., Altes T., Powers P., et. al.: Imaging the lungs in asthmatic patients by using hyperpolarized helium-3 magnetic resonance: assessment of response to methacholine and exercise challenge. J Allergy Clin Immunol 2003; 111: pp. 1205-1211.
5. Woodhouse N., Wild J.M., Paley M.N., et. al.: Combined helium-3/proton magnetic resonance imaging measurement of ventilated lung volumes in smokers compared to never-smokers. J Magn Reson Imaging 2005; 21: pp. 365-369.
6. Kirby M., Heydarian M., Svenningsen S., et. al.: Hyperpolarized 3He magnetic resonance functional imaging semiautomated segmentation. Acad Radiol 2012; 19: pp. 141-152.
7. He M., Kaushik S.S., Robertson S.H., et. al.: Extending semiautomatic ventilation defect analysis for hyperpolarized (129)Xe ventilation MRI. Acad Radiol 2014; 21: pp. 1530-1541.
8. Neder J.A., O’Donnell C.D., Cory J., et. al.: Ventilation distribution heterogeneity at rest as a marker of exercise impairment in mild-to-advanced COPD. COPD 2015; 12: pp. 249-256.
9. American Thoracic Society : Single-breath carbon monoxide diffusing capacity (transfer factor). Recommendations for a standard technique—1995 update. Am J Respir Crit Care Med 1995; 152: pp. 2185-2198.
10. Mata J., Altes T., Truwit J., et. al.: Characterization and detection of physiologic lung changes before and after placement of bronchial valves using hyperpolarized helium-3 MR imaging: preliminary study. Acad Radiol 2011; 18: pp. 1195-1199.
11. Mathew L., Kirby M., Farquhar D., et. al.: Hyperpolarized 3He functional magnetic resonance imaging of bronchoscopic airway bypass in chronic obstructive pulmonary disease. Can Respir J 2012; 19: pp. 41-43.
12. Konno K., Mead J.: Measurement of the separate volume changes of rib cage and abdomen during breathing. J Appl Physiol 1967; 22: pp. 407-422.
13. Pare P.D., Mitzner W.: Airway-parenchymal interdependence. Compr Physiol 2012; 2: pp. 1921-1935.
14. Hughes J.M., Pride N.B.: Examination of the carbon monoxide diffusing capacity (DL(CO)) in relation to its KCO and VA components. Am J Respir Crit Care Med 2012; 186: pp. 132-139.
15. Kruger S.J., Niles D.J., Dardzinski B., et. al.: Hyperpolarized Helium-3 MRI of exercise-induced bronchoconstriction during challenge and therapy. J Magn Reson Imaging 2014; 39: pp. 1230-1237.
16. Kirby M., Pike D., Coxson H.O., et. al.: Hyperpolarized (3)He ventilation defects used to predict pulmonary exacerbations in mild to moderate chronic obstructive pulmonary disease. Radiology 2014; 273: pp. 887-896.
17. Kirby M., Kanhere N., Etemad-Rezai R., et. al.: Hyperpolarized helium-3 magnetic resonance imaging of chronic obstructive pulmonary disease exacerbation. J Magn Reson Imaging 2013; 37: pp. 1223-1227.
18. Saam B.T., Yablonskiy D.A., Kodibagkar V.D., et. al.: MR imaging of diffusion of (3)He gas in healthy and diseased lungs. Magn Reson Med 2000; 44: pp. 174-179.
19. Alshabanat A., Zafari Z., Albanyan O., et. al.: Asthma and COPD overlap syndrome (ACOS): a systematic review and meta analysis. PLoS ONE 2015; 10: pp. e0136065.
20. Fain S.B., Gonzalez-Fernandez G., Peterson E.T., et. al.: Evaluation of structure-function relationships in asthma using multidetector CT and hyperpolarized He-3 MRI. Acad Radiol 2008; 15: pp. 753-762.
21. Cho M.H., Castaldi P.J., Hersh C.P., et. al.: A genome-wide association study of emphysema and airway quantitative imaging phenotypes. Am J Respir Crit Care Med 2015; 192: pp. 559-569.
22. Jarjour N.N., Erzurum S.C., Bleecker E.R., et. al.: Severe asthma: lessons learned from the National Heart, Lung, and Blood Institute Severe Asthma Research Program. Am J Respir Crit Care Med 2012; 185: pp. 356-362.