SCANNER-INDUCED VARIABILITY AND QUALITY ASSURANCE IN LONGITUDINAL DUAL-ENERGY X-RAY ABSORPTIOMETRY MEASUREMENTS

Characteristics of typical malfunctions and scanner-induced variabilit y observed in dual-energy x-ray absorptiometry (DXA), and their potent ial effects on longitudinal reliability of DXA were evaluated. Accordi ng to extensive, cumulative quality assurance (QA) data obtained from two successive x-ray sources during a 3-yr period, the scanner-induced variability may derive from long-term drift (similar to 0.5%/year), s hort-term drift (similar to 0.2%-2.2%/day), inhomogeneity of the x-ray beam intensity over the tabletop (similar to 1%), and changes in inte rnal filtration (similar to 0.5%). The absolute magnitudes of these ef fects may be considerable with respect to expected small changes in bo ne characteristics observed in intervention studies. Furthermore, thes e effects may not be discriminated from each other. Therefore it may n ot be possible to correct their cumulative effect using long-term QA d ata only. The observed drifts are fortunately negligible with respect to the precision adequate for clinical decision making. In contrast, t he evaluation of these multiform scanner-induced variability is warran ted in longitudinal intervention studies using DXA. It is emphasized t hat this study was performed with a single DXA system, and the results should be considered accordingly; nevertheless, it is believed that t he issues raised would apply to other systems too, at least in the sen se of stringent QA. In this respect, single daily phantom measurement appeared to be occasionally ineffective, whereas a remeasurement of th e phantom after subject measurements significantly improved the effect iveness of QA. Altogether, the QA procedures, which consider both the shortterm and long-term variability as well as the spatial variability over the tabletop, may provide a more effective method to detect sile ntly degrading scanner performance and evaluate its effects on the sub ject measurements. High-quality operator performance is, however, a pr erequisite for proper QA in any setting.