PT  - JOURNAL ARTICLE
AU  - J. R. Dame Carroll
AU  - A. Chandra
AU  - A. S. Jones
AU  - N. Berend
AU  - J. S. Magnussen
AU  - G. G. King
TI  - Airway dimensions measured from micro-computed tomography and high-resolution computed tomography
AID  - 10.1183/09031936.06.00012405
DP  - 2006 Oct 01
TA  - European Respiratory Journal
PG  - 712--720
VI  - 28
IP  - 4
4099  - http://erj.ersjournals.com/content/28/4/712.short
4100  - http://erj.ersjournals.com/content/28/4/712.full
SO  - Eur Respir J2006 Oct 01; 28
AB  - Volume averaging results in both over- and underestimation of airway dimensions when they are measured by high-resolution computed tomography (HRCT). The current authors calibrated computerised measurements of airway dimensions from HRCT against a novel three-dimensional micro-computed tomography (CT) standard, which has a 50-fold greater resolution, as well as against traditional morphometry. Inflation-fixed porcine lung cubes were scanned by HRCT and micro-CT. A total of 59 lumen area (Ai), 30 wall area (Aaw) and 11 lumen volume (Vi) measurements were made. Ai was measured from the cut surface of 11 airways by morphometry. Airways in scanned images were matched using branching points. After calibration, the errors of Ai, Aaw and Vi HRCT measurements were determined. The current authors found a systematic, size-dependent underestimation of Ai and overestimation of Aaw from HRCT measurements. This was used to calibrate an HRCT measurement algorithm. The 95% limits of agreement of subsequent measurements were ±3.2 mm2 for Ai, ±4.3 mm2 for Aaw, and ±11.2 mm3 for Vi with no systematic error. Morphometric measurements agreed with micro-CT (±2.5 mm2) without systematic error. In conclusion, micro-computed tomography image data from inflation-fixed airways can be used as calibration standards for three-dimensional lumen volume measurements from high-resolution computed tomography, while morphometry is acceptable for two-dimensional measurements. The image dataset could be used to validate other developmental three-dimensional segmentation algorithms.