European Radiology

, Volume 26, Issue 5, pp 1368–1377 | Cite as

Perfusion- and pattern-based quantitative CT indexes using contrast-enhanced dual-energy computed tomography in diffuse interstitial lung disease: relationships with physiologic impairment and prediction of prognosis

  • Jung Won Moon
  • Jang Pyo Bae
  • Ho Yun LeeEmail author
  • Namkug KimEmail author
  • Man Pyo Chung
  • Hye Yun Park
  • Yongjun Chang
  • Joon Beom Seo
  • Kyung Soo Lee
Computed Tomography



To evaluate automated texture-based segmentation of dual-energy CT (DECT) images in diffuse interstitial lung disease (DILD) patients and prognostic stratification by overlapping morphologic and perfusion information of total lung.


Suspected DILD patients scheduled for surgical biopsy were prospectively included. Texture patterns included ground-glass opacity (GGO), reticulation and consolidation. Pattern- and perfusion-based CT measurements were assessed to extract quantitative parameters. Accuracy of texture-based segmentation was analysed. Correlations between CT measurements and pulmonary function test or 6-minute walk test (6MWT) were calculated. Parameters of idiopathic pulmonary fibrosis/usual interstitial pneumonia (IPF/UIP) and non-IPF/UIP were compared. Survival analysis was performed.


Overall accuracy was 90.47 % for whole lung segmentation. Correlations between mean iodine values of total lung, 50–97.5th (%) attenuation and forced vital capacity or 6MWT were significant. Volume of GGO, reticulation and consolidation had significant correlation with DLco or SpO2 on 6MWT. Significant differences were noted between IPF/UIP and non-IPF/UIP in 6MWT distance, mean iodine value of total lung, 25–75th (%) attenuation and entropy. IPF/UIP diagnosis, GGO ratio, DILD extent, 25–75th (%) attenuation and SpO2 on 6MWT showed significant correlations with survival.


DECT combined with pattern analysis is useful for analysing DILD and predicting survival by provision of morphology and enhancement.

Key Points

• Dual-energy CT (DECT) produces morphologic and parenchymal enhancement information.

• Automated lung segmentation enables analysis of disease extent and severity.

• This prospective study showed value of DECT in DILD patients.

• Parameters on DECT enable characterization and survival prediction of DILD.


DECT DILD Perfusion- or pattern-based CT quantification parameters IPF/UIP Survival 



dual-energy computed tomography


diffuse interstitial lung disease


ground-glass opacity


high-resolution computed tomography


idiopathic pulmonary fibrosis/usual interstitial pneumonias


pulmonary function test


6-minute walk test


virtual non-contrast



The scientific guarantor of this publication is Ho Yun Lee. 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 research was supported by the Research Fund of the GE Healthcare (OMS-09-07). One of the authors has significant statistical expertise. Institutional review board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. Some study subjects or cohorts have not been previously reported. Methodology: prospective, diagnostic or prognostic study, performed at one institution.

Supplementary material

330_2015_3946_MOESM1_ESM.docx (18 kb)
ESM 1 (DOCX 18 kb)


  1. 1.
    Schaefer-Prokop C, Prokop M, Fleischmann D, Herold C (2001) High-resolution CT of diffuse interstitial lung disease: key findings in common disorders. Eur Radiol 11:373–392CrossRefPubMedGoogle Scholar
  2. 2.
    American Thoracic Society (2000) Idiopathic pulmonary fibrosis: diagnosis and treatment. International consensus statement. American Thoracic Society (ATS), and the European Respiratory Society (ERS). Am J Respir Crit Care Med 161:646–664CrossRefGoogle Scholar
  3. 3.
    Walsh SL, Hansell DM (2010) Diffuse interstitial lung disease: overlaps and uncertainties. Eur Radiol 20:1859–1867CrossRefPubMedGoogle Scholar
  4. 4.
    Hartman TE, Primack SL, Kang EY et al (1996) Disease progression in usual interstitial pneumonia compared with desquamative interstitial pneumonia. Assessment with serial CT. Chest 110:378–382CrossRefPubMedGoogle Scholar
  5. 5.
    Remy-Jardin M, Faivre JB, Pontana F et al (2010) Thoracic applications of dual energy. Radiol Clin North Am 48:193–205CrossRefPubMedGoogle Scholar
  6. 6.
    Martinez FJ, Flaherty K (2006) Pulmonary function testing in idiopathic interstitial pneumonias. Proc Am Thorac Soc 3:315–321CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Lama VN, Flaherty KR, Toews GB et al (2003) Prognostic value of desaturation during a 6-minute walk test in idiopathic interstitial pneumonia. Am J Respir Crit Care Med 168:1084–1090CrossRefPubMedGoogle Scholar
  8. 8.
    Bae J, Kim N, Lee SM, Seo JB, Kim HC (2014) Thoracic cavity segmentation algorithm using multiorgan extraction and surface fitting in volumetric CT. Med Phys 41:041908CrossRefPubMedGoogle Scholar
  9. 9.
    National Library of Medicine Insight Segmentation and Registration Toolkit (ITK)Google Scholar
  10. 10.
    Kim N, Seo JB, Lee Y, Lee JG, Kim SS, Kang SH (2009) Development of an automatic classification system for differentiation of obstructive lung disease using HRCT. J Digit Imaging 22:136–148CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Chang Y, Lim J, Kim N, Seo JB, Lynch DA (2013) A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: comparison to a Bayesian classifier. Med Phys 40:051912CrossRefPubMedGoogle Scholar
  12. 12.
    Best AC, Lynch AM, Bozic CM, Miller D, Grunwald GK, Lynch DA (2003) Quantitative CT indexes in idiopathic pulmonary fibrosis: relationship with physiologic impairment. Radiology 228:407–414CrossRefPubMedGoogle Scholar
  13. 13.
    Maldonado F, Moua T, Rajagopalan S et al (2014) Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis. Eur Respir J 43:204–212CrossRefPubMedGoogle Scholar
  14. 14.
    Best AC, Meng J, Lynch AM et al (2008) Idiopathic pulmonary fibrosis: physiologic tests, quantitative CT indexes, and CT visual scores as predictors of mortality. Radiology 246:935–940CrossRefPubMedGoogle Scholar
  15. 15.
    Pu J, Roos J, Yi CA, Napel S, Rubin GD, Paik DS (2008) Adaptive border marching algorithm: automatic lung segmentation on chest CT images. Comput Med Imaging Graph 32:452–462CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Sluimer I, Prokop M, van Ginneken B (2005) Toward automated segmentation of the pathological lung in CT. IEEE Trans Med Imaging 24:1025–1038CrossRefPubMedGoogle Scholar
  17. 17.
    Hoey ET, Mirsadraee S, Pepke-Zaba J, Jenkins DP, Gopalan D, Screaton NJ (2011) Dual-energy CT angiography for assessment of regional pulmonary perfusion in patients with chronic thromboembolic pulmonary hypertension: initial experience. AJR Am J Roentgenol 196:524–532CrossRefPubMedGoogle Scholar
  18. 18.
    Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA (2010) Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging 10:137–143CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Travis WD, Matsui K, Moss J, Ferrans VJ (2000) Idiopathic nonspecific interstitial pneumonia: prognostic significance of cellular and fibrosing patterns: survival comparison with usual interstitial pneumonia and desquamative interstitial pneumonia. Am J Surg Pathol 24:19–33CrossRefPubMedGoogle Scholar
  20. 20.
    Nicholson AG, Colby TV, du Bois RM, Hansell DM, Wells AU (2000) The prognostic significance of the histologic pattern of interstitial pneumonia in patients presenting with the clinical entity of cryptogenic fibrosing alveolitis. Am J Respir Crit Care Med 162:2213–2217CrossRefPubMedGoogle Scholar
  21. 21.
    Kim RJ, Wu E, Rafael A et al (2000) The use of contrast-enhanced magnetic resonance imaging to identify reversible myocardial dysfunction. N Engl J Med 343:1445–1453CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2015

Authors and Affiliations

  • Jung Won Moon
    • 1
  • Jang Pyo Bae
    • 2
  • Ho Yun Lee
    • 3
    Email author
  • Namkug Kim
    • 2
    Email author
  • Man Pyo Chung
    • 4
  • Hye Yun Park
    • 4
  • Yongjun Chang
    • 2
  • Joon Beom Seo
    • 2
  • Kyung Soo Lee
    • 3
  1. 1.Department of Radiology (J.W.M.), Kangbuk Samsung HospitalSungkyunkwan University School of MedicineSeoulKorea
  2. 2.Department of Radiology (J.P.B., N.K., Y.C., J.B.S.), Asan Medical CenterUniversity of Ulsan College of MedicineSeoulKorea
  3. 3.Department of Radiology and Center for Imaging Science (H.Y.L., K.S.L.), Samsung Medical CenterSungkyunkwan University School of MedicineSeoulKorea
  4. 4.Department of Pulmonology (M.P.C, H.Y.P), Samsung Medical CenterSungkyunkwan University School of MedicineSeoulKorea

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