Reproducibility of intensity-based estimates of lung ventilation

Kaifang Du, John E. Bayouth, Kai Ding, Gary E. Christensen, Kunlin Cao, Joseph M. Reinhardt

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Purpose: Lung function depends on lung expansion and contraction during the respiratory cycle. Respiratory-gated CT imaging and image registration can be used to estimate the regional lung volume change by observing CT voxel density changes during inspiration or expiration. In this study, the authors examine the reproducibility of intensity-based estimates of lung tissue expansion and contraction in three mechanically ventilated sheep and ten spontaneously breathing humans. The intensity-based estimates are compared to the estimates of lung function derived from image registration deformation field. Methods: 4DCT data set was acquired for a cohort of spontaneously breathing humans and anesthetized and mechanically ventilated sheep. For each subject, two 4DCT scans were performed with a short time interval between acquisitions. From each 4DCT data set, an image pair consisting of a volume reconstructed near end inspiration and a volume reconstructed near end exhalation was selected. The end inspiration and end exhalation images were registered using a tissue volume preserving deformable registration algorithm. The CT density change in the registered image pair was used to compute intensity-based specific air volume change (SAC) and the intensity-based Jacobian (IJAC), while the transformation-based Jacobian (TJAC) was computed directly from the image registration deformation field. IJAC is introduced to make the intensity-based and transformation-based methods comparable since SAC and Jacobian may not be associated with the same physiological phenomenon and have different units. Scan-to-scan variations in respiratory effort were corrected using a global scaling factor for normalization. A gamma index metric was introduced to quantify voxel-by-voxel reproducibility considering both differences in ventilation and distance between matching voxels. The authors also tested how different CT prefiltering levels affected intensity-based ventilation reproducibility. Results: Higher reproducibility was found for anesthetized mechanically ventilated animals than for the humans for both the intensity-based (IJAC) and transformation-based (TJAC) ventilation estimates. The human IJAC maps had scan-to-scan correlation coefficients of 0.45 ± 0.14, a gamma pass rate 70 ± 8 without normalization and 75 ± 5 with normalization. The human TJAC maps had correlation coefficients 0.81 ± 0.10, a gamma pass rate 86 ± 11 without normalization and 93 ± 4 with normalization. The gamma pass rate and correlation coefficient of the IJAC maps gradually increased with increased smoothing, but were still much lower than those of the TJAC maps. Conclusions: The transformation-based ventilation maps show better reproducibility than the intensity-based maps, especially in human subjects. Reproducibility was also found to depend on variations in respiratory effort; all techniques were better when applied to images from mechanically ventilated sheep compared to spontaneously breathing human subjects. Nevertheless, intensity-based techniques applied to mechanically ventilated sheep were less reproducible than the transformation-based applied to spontaneously breathing humans, suggesting the method used to determine ventilation maps is important. Prefiltering of the CT images may help to improve the reproducibility of the intensity-based ventilation estimates, but even with filtering the reproducibility of the intensity-based ventilation estimates is not as good as that of transformation-based ventilation estimates.

Original languageEnglish (US)
Article number063504
JournalMedical physics
Issue number6
StatePublished - Jun 2013


  • image registration
  • intensity-based
  • lung ventilation
  • regional pulmonary function
  • reproducibility

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging


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