European Radiology

, Volume 27, Issue 7, pp 2818–2827 | Cite as

Assessment of regional emphysema, air-trapping and Xenon-ventilation using dual-energy computed tomography in chronic obstructive pulmonary disease patients

  • Sang Min Lee
  • Joon Beom Seo
  • Hye Jeon Hwang
  • Namkug Kim
  • Sang Young Oh
  • Jae Seung Lee
  • Sei Won Lee
  • Yeon-Mok Oh
  • Tae Hoon Kim
Chest

Abstract

Objectives

To compare the parenchymal attenuation change between inspiration/expiration CTs with dynamic ventilation change between xenon wash-in (WI) inspiration and wash-out (WO) expiration CTs.

Methods

52 prospectively enrolled COPD patients underwent xenon ventilation dual-energy CT during WI and WO periods and pulmonary function tests (PFTs). The parenchymal attenuation parameters (emphysema index (EI), gas-trapping index (GTI) and air-trapping index (ATI)) and xenon ventilation parameters (xenon in WI (Xe-WI), xenon in WO (Xe-WO) and xenon dynamic (Xe-Dyna)) of whole lung and three divided areas (emphysema, hyperinflation and normal) were calculated on virtual non-contrast images and ventilation images. Pearson correlation, linear regression analysis and one-way ANOVA were performed.

Results

EI, GTI and ATI showed a significant correlation with Xe-WI, Xe-WO and Xe-Dyna (EI R = −.744, −.562, −.737; GTI R = −.621, −.442, −.629; ATI R = −.600, −.421, −.610, respectively, p < 0.01). All CT parameters showed significant correlation with PFTs except forced vital capacity (FVC). There was a significant difference in GTI, ATI and Xe-Dyna in each lung area (p < 0.01).

Conclusions

The parenchymal attenuation change between inspiration/expiration CTs and xenon dynamic change between xenon WI- and WO-CTs correlate significantly. There are alterations in the dynamics of xenon ventilation between areas of emphysema.

Key Points

The xenon ventilation change correlates with the parenchymal attenuation change.

The xenon ventilation change shows the difference between three lung areas.

The combination of attenuation and xenon can predict more accurate PFTs.

Keywords

Chronic obstructive pulmonary disease Air trapping Xenon Ventilation Dual-energy CT 

Abbreviations

6MWT

6-minute walking distance test

ATI

Air-trapping index

COPD

Chronic obstructive pulmonary disease

DLCO

Diffusing capacity of lung for carbon monoxide

EI

Emphysema index

FEF25-75%

Mean forced expiratory flow between 25% and 75% of FVC

FEV1

Forced expiratory volume in 1 second

FVC

Forced vital capacity

GOLD

Global Initiative for Chronic Obstructive Lung Disease

GTI

Gas-trapping index

KOLD

Korean Obstructive Lung Disease

MLD

Mean lung density

PFTs

Pulmonary function tests

RV

Residual volume

TLC

Total lung capacity

WI

Wash-in

WO

Wash-out

Notes

Acknowledgements

The scientific guarantor of this publication is Dr. Joon Beom Seo. The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. This study received funding from the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (no. A111599). The authors thank Jung Bok Lee, PhD, for statistical assistance, and Yongjun Chang, PhD, Taekjin Jang, BS, Soo Jin Park, BS, Junghye Kang, BS and Heejun Park, BS, for their technical assistance. Institutional Review Board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. Some study subjects or cohorts have been previously reported in Hwang et al. (2015) Invest Radiol. 51:306-15

The paper is a prospective, diagnostic or prognostic study, and was performed at one institution.

References

  1. 1.
    Hogg JC, Chu F, Utokaparch S et al (2004) The nature of small-airway obstruction in chronic obstructive pulmonary disease. New Engl J Med 350:2645–53CrossRefPubMedGoogle Scholar
  2. 2.
    Hogg JC, McDonough JE, Suzuki M (2013) Small airway obstruction in COPD: new insights based on micro-CT imaging and MRI imaging. Chest 143:1436–43CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Arakawa H, Webb WR (1998) Air trapping on expiratory high-resolution CT scans in the absence of inspiratory scan abnormalities: correlation with pulmonary function tests and differential diagnosis. AJR Am J Roentgenol 170:1349–53CrossRefPubMedGoogle Scholar
  4. 4.
    Lucidarme O, Grenier PA, Cadi M, Mourey-Gerosa I, Benali K, Cluzel P (2000) Evaluation of air trapping at CT: comparison of continuous-versus suspended-expiration CT techniques. Radiology 216:768–72CrossRefPubMedGoogle Scholar
  5. 5.
    Ding K, Cao K, Fuld MK et al (2012) Comparison of image registration based measures of regional lung ventilation from dynamic spiral CT with Xe-CT. Med Phys 39:5084–98CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Choi S, Hoffman EA, Wenzel SE et al (2013) Registration-based assessment of regional lung function via volumetric CT images of normal subjects vs. severe asthmatics. J Appl Physiol 115:730–42CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Kim EY, Seo JB, Oh SY et al (2014) Assessment of perfusion pattern and extent of perfusion defect on dual-energy CT angiography: correlations between the causes of pulmonary hypertension and vascular parameters. Korean J Radiol 15:286–94CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Simon BA (2005) Regional ventilation and lung mechanics using X-Ray CT. Acad Radiol 12:1414–22CrossRefPubMedGoogle Scholar
  9. 9.
    Kim WW, Lee CH, Goo JM et al (2012) Xenon-enhanced dual-energy CT of patients with asthma: dynamic ventilation changes after methacholine and salbutamol inhalation. AJR Am J Roentgenol 199:975–81CrossRefPubMedGoogle Scholar
  10. 10.
    Park EA, Goo JM, Park SJ et al (2010) Chronic obstructive pulmonary disease: quantitative and visual ventilation pattern analysis at xenon ventilation CT performed by using a dual-energy technique. Radiology 256:985–97CrossRefPubMedGoogle Scholar
  11. 11.
    Hwang HJ, Seo JB, Lee SM et al (2015) Assessment of regional xenon ventilation, perfusion, and ventilation-perfusion mismatch using dual-energy computed tomography in chronic obstructive pulmonary disease patients. Investig Radiol 51:306–15Google Scholar
  12. 12.
    Honda N, Osada H, Watanabe W et al (2012) Imaging of ventilation with dual-energy CT during breath hold after single vital-capacity inspiration of stable xenon. Radiology 262(1):262–8CrossRefPubMedGoogle Scholar
  13. 13.
    Kim EY, Seo JB, Lee HJ et al (2015) Detailed analysis of the density change on chest CT of COPD using non-rigid registration of inspiration/expiration CT scans. Eur Radiol 25:541–9CrossRefPubMedGoogle Scholar
  14. 14.
    Lee SM, Seo JB, Lee SM et al (2015) Optimal threshold of subtraction method for quantification of air-trapping on coregistered CT in COPD patients. Eur Radiol 26:2184–92CrossRefPubMedGoogle Scholar
  15. 15.
    Lynch DA, Al-Qaisi MA (2013) Quantitative computed tomography in chronic obstructive pulmonary disease. J Thorac Imaging 28:284–90CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Murphy K, Pluim JP, van Rikxoort EM et al (2012) Toward automatic regional analysis of pulmonary function using inspiration and expiration thoracic CT. Med Phys 39:1650–62CrossRefPubMedGoogle Scholar
  17. 17.
    Goo HW, Chae EJ, Seo JB, Hong SJ (2008) Xenon ventilation CT using a dual-source dual-energy technique: dynamic ventilation abnormality in a child with bronchial atresia. Pediatr Radiol 38:1113–6CrossRefPubMedGoogle Scholar
  18. 18.
    Goo HW, Yang DH, Hong SJ et al (2010) Xenon ventilation CT using dual-source and dual-energy technique in children with bronchiolitis obliterans: correlation of xenon and CT density values with pulmonary function test results. Pediatr Radiol 40:1490–7CrossRefPubMedGoogle Scholar
  19. 19.
    Reymond E, Jankowski A, Pison C et al (2013) Prediction of lobar collateral ventilation in 25 patients with severe emphysema by fissure analysis with CT. AJR Am J Roentgenol 201:W571–5CrossRefPubMedGoogle Scholar
  20. 20.
    Lee CW, Seo JB, Lee Y et al (2012) A pilot trial on pulmonary emphysema quantification and perfusion mapping in a single-step using contrast-enhanced dual-energy computed tomography. Investig Radiol 47:92–7CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Sang Min Lee
    • 1
    • 2
  • Joon Beom Seo
    • 1
  • Hye Jeon Hwang
    • 1
    • 3
  • Namkug Kim
    • 1
  • Sang Young Oh
    • 1
  • Jae Seung Lee
    • 4
  • Sei Won Lee
    • 4
  • Yeon-Mok Oh
    • 4
  • Tae Hoon Kim
    • 2
  1. 1.Division of Cardiothoracic Radiology, Department of Radiology and Research Institute of Radiology, Asan Medical CenterUniversity of Ulsan College of MedicineSeoulRepublic of Korea
  2. 2.Department of Radiology, Research Istitute of Radiological ScienceYonsei University College of Medicine, Gangnam Severance HospitalSeoulRepublic of Korea
  3. 3.Department of Radiology, Hallym University College of MedicineHallym University Sacred Heart HospitalAnyang-siRepublic of Korea
  4. 4.Department of Pulmonary and Critical Care Medicine, and Clinical Research Center for Chronic Obstructive Airway Diseases, Asan Medical CenterUniversity of Ulsan College of MedicineSeoulRepublic of Korea

Personalised recommendations