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Optimizing HR-pQCT workflow: a comparison of bias and precision error for quantitative bone analysis
D E Whittier1, A N Mudryk1, I D Vandergaag1
1McCaig Institute for Bone and Joint Health and Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Summary
Manual correction of automated contours in high-resolution peripheral quantitative computed tomography (HPQCT) is necessary to minimize bias. Operator training significantly impacts precision error, with expert operators achieving reliable results.
Area of Science:
- Bone densitometry
- Medical imaging analysis
- Skeletal research
Background:
- High-resolution peripheral quantitative computed tomography (HPQCT) analysis involves manual correction of automated contours.
- Minimizing manual corrections is desirable for efficiency and precision, but the bias of automated protocols is unknown.
- This study investigates bias in uncorrected contours and the effect of operator training on bias and precision.
Purpose of the Study:
- To quantify bias in uncorrected contours from automated protocols in HPQCT.
- To assess the impact of operator training on bias and precision error in contour correction.
- To provide guidance on optimizing HPQCT workflow efficiency.
Main Methods:
- Analysis of 45 radii and tibiae scans across varying bone densities.
- Comparison of automated contours with manually corrected contours from three operators with different training levels.
- Estimation of bias against a "ground truth" and measurement of inter-operator precision.
Main Results:
- Tibia scans exhibited greater error bias than radius scans with uncorrected contours, particularly in bone mineral density and cortical microarchitecture.
- Bias and precision errors were highest with a beginner operator; removing this operator eliminated bias.
- Inter-operator precision ranged from 0.01% to 3.74% for most parameters, excluding cortical porosity.
Conclusions:
- Manual correction of automated contours in HPQCT is essential for accurate results.
- Adequate operator training is crucial for minimizing bias and maximizing workflow efficiency in HPQCT analysis.
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