Optimizing HR-pQCT workflow: a comparison of bias and precision error for quantitative bone analysis.

Manual correction of automatically generated contours for high-resolution peripheral quantitative computed tomography can be time consuming and introduces precision error. However, bias related to the automated protocol is unknown. This study provides insight into error bias that is present when using uncorrected contours and inter-operator precision error based on operator training. High-resolution peripheral quantitative computed tomography workflow includes manually correcting contours generated by the manufacturer's automated protocol. There is interest in minimizing corrections to save time and reduce precision error; however, bias related to the automated protocol is unknown. This study quantifies error bias when contours are uncorrected and identifies the impact of operator training on bias and precision error. Forty-five radii and tibiae scans across a representative range of bone density were analyzed using the automated and manually corrected contours of three operators, with training ranging from beginner to expert, and compared with a "ground truth" to estimate bias. Inter-operator precision was measured across operators. The tibia had greater error bias than the radius when contours were uncorrected, with compartmental bone mineral densities and cortical microarchitecture having greatest biases, which could have significant implications for interpretation of studies using this skeletal site. Bias and precision error were greatest when contours were corrected by the beginner operator; however, when this operator was removed, bias was no longer present and inter-operator precision was between 0.01 and 3.74% for all parameters except cortical porosity. These findings establish the need for manual correction and provide guidance on operator training needed to maximize workflow efficiency.