An assessment of the correlation between robust CT-derived ventilation and pulmonary function test in a cohort with no respiratory symptoms

Br J Radiol. 2021 Feb 1;94(1118):20201218. doi: 10.1259/bjr.20201218. Epub 2020 Dec 15.


Objective: To evaluate CT-ventilation imaging (CTVI) within a well-characterized, healthy cohort with no respiratory symptoms and examine the correlation between CTVI and concurrent pulmonary function test (PFT).

Methods: CT scans and PFTs from 77 Caucasian participants in the NORM dataset ( NCT00848406) were analyzed. CTVI was generated using the robust Integrated Jacobian Formulation (IJF) method. IJF estimated total lung capacity (TLC) was computed from CTVI. Bias-adjusted Pearson's correlation between PFT and IJF-based TLC was computed.

Results: IJF- and PFT-measured TLC showed a good correlation for both males and females [males: 0.657, 95% CI (0.438-0.797); females: 0.667, 95% CI (0.416-0.817)]. When adjusting for age, height, smoking, and abnormal CT scan, correlation moderated [males: 0.432, 95% CI (0.129-0.655); females: 0.540, 95% CI (0.207-0.753)]. Visual inspection of CTVI revealed participants who had functional defects, despite the fact that all participant had normal high-resolution CT scan.

Conclusion: In this study, we demonstrate that IJF computed CTVI has good correlation with concurrent PFT in a well-validated patient cohort with no respiratory symptoms.

Advances in knowledge: IJF-computed CTVI's overall numerical robustness and consistency with PFT support its potential as a method for providing spatiotemporal assessment of high and low function areas on volumetric non-contrast CT scan.

MeSH terms

  • Adult
  • Cohort Studies
  • Cross-Sectional Studies
  • Female
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Lung / physiology*
  • Male
  • Reference Values
  • Reproducibility of Results
  • Respiratory Function Tests / methods*
  • Respiratory Function Tests / statistics & numerical data*
  • Tomography, X-Ray Computed / methods*

Associated data