Influence of Calibration on Densitometric Studies of Emphysema Progression Using Computed Tomography

• DOI: 10.1164/rccm.200403-326oc
• Publication Date: 2004-07-22T02:49:02Z
• Authors: David G. Parr; Berend C. Stoel; Jan Stolk; Peter G. Nightingale; Robert A. Stockley
• Keywords: Time Factors; Tomography Scanners, X-Ray Computed; Phantoms, Imaging; Air; Reproducibility of Results; Middle Aged; 3. Good health; 03 medical and health sciences; Absorptiometry, Photon; Dogs; 0302 clinical medicine; Pulmonary Emphysema; alpha 1-Antitrypsin Deficiency; Calibration; Disease Progression; Animals; Humans; Longitudinal Studies; Tomography, X-Ray Computed; Lung

The fundamental importance of calibration for any measuring device is indisputable, but computed tomography (CT) calibration in longitudinal lung densitometry studies is largely unexplored. Although the validity of CT as a measure of emphysema has been confirmed in cross-sectional studies, there are limited data on long-term reproducibility, and this is critically important for validating its use as an outcome measure in therapeutic trials. A general understanding of the strengths and pitfalls of CT densitometry is critical for physicians reviewing the published literature using this methodology. In our study of 57 patients with alpha-1 antitrypsin deficiency (phenotype PiZ), progression of voxel index determined from three successive annual scans acquired with a fully calibrated scanner was intimately associated with changes in CT air densitometry, sampled from patient images. Images were therefore reanalyzed, using a correction technique validated in phantom studies that adjusted for changes in measured air density, and the reliability of the voxel index as a measure of emphysema progression was improved. Comparison of adjusted voxel index thresholds indicated the optimum threshold was -950 Hounsfield units. Internal air calibration is therefore critical in longitudinal and multicenter lung densitometry studies of emphysema and incorporation of a correction factor is essential for quantitative image analysis.