Effect of abdominal adipose content on spine phantom bone mineral density measured by rapid kilovoltage-switching dual-energy CT and quantitative CT

BACKGROUND: The purpose of this study was to investigate the effect of abdominal adiposity, as measured by abdominal total adipose tissue (TAT), on the accuracy of rapid kilovoltage-switching dual-energy computed tomography (DECT) and quantitative computed tomography (QCT) measurements of bone mineral density (BMD) in a spine phantom model. METHODS: Fresh porcine fat was wrapped around the European Spine Phantom (ESP) and divided into four groups according to the TAT cross-sectional areas, S=0, 100, 200, and 350 cm(2), to simulate different TAT contents. The hydroxyapatite (HAP) (water) values of each vertebra were measured by DECT, and the BMD values by QCT. A one-sample t-test was used to analyze the differences between the measurements and the true values of the ESP. One-way analysis of variance (ANOVA) was applied to compare the differences between measurements under different TAT conditions, and the root-mean-square errors (RMSEs) of the BMD measurements were calculated and compared. Moreover, Pearson’s correlation analysis was performed for the RMSE and TAT. Linear regression analysis was conducted on the measurements, the true values, and the TAT to obtain the correction equations for the BMD and to compare the RMSE before and after correction. RESULTS: At higher TAT content, the measurements of both scanning methods were more affected, and the measurements of the TAT =350 cm(2) group were significantly different from the remaining groups (P<0.05). There was a positive correlation between the RMSE and TAT (r>0, P<0.05), with the RMSE of the L1 vertebrae the largest under the same TAT content. The corrected equations for BMD were derived, and the RMSE of BMD was significantly reduced after correction. CONCLUSIONS: The measurements of ESP BMD for both rapid kilovoltage-switching DECT and QCT changed with TAT content. Along with the increase of TAT, the RMSE of measurements increased and the accuracy decreased; moreover, the lower the value of BMD, the more significant the RMSE. The linear regression analysis allowed the corrected BMD measurements to be very close to true values.