Optimal threshold in CT quantification of emphysema

Zhimin Wang, Suicheng Gu, Joseph K. Leader, Shinjini Kundu, John R. Tedrow, Frank C. Sciurba, David Gur, Jill M. Siegfried, Jiantao Pu

Research output: Contribution to journalArticlepeer-review

90 Scopus citations


Objectives: To determine the optimal threshold by quantitatively assessing the extent of emphysema at the level of the entire lung and at the level of individual lobes using a large, diverse dataset of computed tomography (CT) examinations. Methods: This study comprises 573 chest CT examinations acquired from subjects with different levels of airway obstruction (222 none, 83 mild, 141 moderate, 63 severe and 64 very severe). The extent of emphysema was quantified using the percentage of the low attenuation area (LAA%) divided by the total lung or lobe volume(s). The correlations between the extent of emphysema, and pulmonary functions and the five-category classification were assessed using Pearson and Spearman's correlation coefficients, respectively. When quantifying emphysema using a density mask, a wide range of thresholds from -850 to -1,000 HU were used. Results: The highest correlations of LAA% with the five-category classification and PFT measures ranged from -925 to -965 HU for each individual lobe and the entire lung. However, the differences between the highest correlations and those obtained at -950 HU are relatively small. Conclusion: Although there are variations in the optimal cut-off thresholds for individual lobes, the single threshold of -950 HU is still an acceptable threshold for density-based emphysema quantification. Key Points: • CT is widely used to assess the severity of emphysema • Density mask technique helps clinicians assess the extent of emphysema with CT • A standardised cut-off for density mask analysis at lobe level is desirable • -950 HU is acceptable for density-based emphysema quantification at the lobar level

Original languageEnglish (US)
Pages (from-to)975-984
Number of pages10
JournalEuropean Radiology
Issue number4
StatePublished - Apr 2013

Bibliographical note

Funding Information:
This work was supported in part by grants HL096613, CA090440, HL084948, HL095397, 2012KTCL03-07 to the University of Pittsburgh from the National Institute of Health, the Bonnie J. Addario Lung Cancer Foundation, and the SPORE in Lung Cancer Career Development Program.


  • Chronic obstructive pulmonary disease
  • Computed tomography
  • Density mask
  • Lobe segmentation
  • Pulmonary emphysema


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