Airway diseases (e.g., asthma, emphysema, and chronic bronchitis) are extremely common worldwide. Any morphological variations (abnormalities) of airways may physically change airflow and ultimately affect the ability of the lungs in gas exchange. In this study, we describe a novel algorithm aimed to automatically identify airway walls depicted on CT images. The underlying idea is to place a three-dimensional (3D) surface model within airway regions and thereafter allow this model to evolve (deform) under predefined external and internal forces automatically to the location where these forces reach a state of balance. By taking advantage of the geometric and the density characteristics of airway walls, the evolution procedure is performed in a distance gradient field and ultimately stops at regions with the highest contrast. The performance of this scheme was quantitatively evaluated from several perspectives. First, we assessed the accuracy of the developed scheme using a dedicated lung phantom in airway wall estimation and compared it with the traditional full-width at half maximum (FWHM) method. The phantom study shows that the developed scheme has an error ranging from 0.04. mm to 0.36. mm, which is much smaller than the FWHM method with an error ranging from 0.16. mm to 0.84. mm. Second, we compared the results obtained by the developed scheme with those manually delineated by an experienced (>30. years) radiologist on clinical chest CT examinations, showing a mean difference of 0.084. mm. In particular, the sensitivity of the scheme to different reconstruction kernels was evaluated on real chest CT examinations. For the 'lung', 'bone' and 'standard' kernels, the average airway wall thicknesses computed by the developed scheme were 1.302. mm, 1.333. mm and 1.339. mm, respectively. Our preliminary experiments showed that the scheme had a reasonable accuracy in airway wall estimation. For a clinical chest CT examination, it took around 4. min for this scheme to identify the inner and outer airway walls on a modern PC.
|Original language||English (US)|
|Number of pages||14|
|Journal||Medical Image Analysis|
|State||Published - Apr 2013|
Bibliographical noteFunding 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.
- Active surface
- Airway wall
- Gradient distance field