Fixed and Relative Positioning of Scans for High Resolution Peripheral Quantitative Computed Tomography.

INTRODUCTION: High resolution peripheral quantitative computed tomography (HR-pQCT) imaging protocol requires defining where to position the ∼1 cm thick scan along the bone length. Discrepancies between the use of two positioning methods, the relative and fixed offset, may be problematic in the comparison between studies and participants. This study investigated how bone landmarks scale linearly with length and how this scaling affects both positioning methods aimed at providing a consistent anatomical location for scan acquisition. METHODS: Using CT images of the radius (N = 25) and tibia (N = 42), 10 anatomical landmarks were selected along the bone length. The location of these landmarks was converted to a percent length along the bone, and the variation in their location was evaluated across the dataset. The absolute location of the HR-pQCT scan position using both offset methods was identified for all bones and converted to a percent length position relative to the HR-pQCT reference line for comparison. A secondary analysis of the location of the scan region specifically within the metaphysis was explored at the tibia. RESULTS: The location of landmarks deviated from a linear relationship across the dataset, with a range of 3.6 % at the radius sites, and 4.5 % at the tibia sites. The consequent variation of the position of the scan at the radius was 0.6 % and 0.3 %, and at the tibia 2.4 % and 0.5 %, for the fixed and relative offset, respectively. The position of the metaphyseal junction with the epiphysis relative to the scan position was poorly correlated to bone length, with R2 = 0.06 and 0.37, for the fixed and relative offset respectively. CONCLUSION: The variation of the scan position by either method is negated by the intrinsic variation of the bone anatomy with respect both to total bone length as well as the metaphyseal region. Therefore, there is no clear benefit of either offset method. However, the lack of difference due to the inherent variation in the underlying anatomy implies that it is reasonable to compare studies even if they are using different positioning methods.

Transferability of bone phenotyping and fracture risk assessment by µFRAC from first-generation high-resolution peripheral quantitative computed tomography to second-generation scan data.

INTRODUCTION: The continued development of high-resolution peripheral quantitative computed tomography (HR-pQCT) has led to a second-generation scanner with higher resolution and longer scan region. However, large multicenter prospective cohorts were collected with first-generation HR-pQCT and have been used to develop bone phenotyping and fracture risk prediction (µFRAC) models. This study establishes whether there is sufficient universality of these first-generation trained models for use with second-generation scan data. METHODS: HR-pQCT data were collected for a cohort of 60 individuals, who had been scanned on both first- and second-generation scanners on the same day to establish the universality of the HR-pQCT models. These data were each used as input to first-generation trained bone microarchitecture models for bone phenotyping and fracture risk prediction, and their outputs were compared for each study participant. Reproducibility of the models were assessed using same-day repeat scans obtained from first-generation (n = 37) and second-generation (n = 74) scanners. RESULTS: Across scanner generations, the bone phenotyping model performed with an accuracy of 93.1%. Similarly, the 5-year fracture risk assessment by µFRAC was well correlated with a Pearson's (r) correlation coefficient of r > 0.83 for the three variations of µFRAC (varying inclusion of clinical risk factors, finite element analysis, and dual X-ray absorptiometry). The first-generation reproducibility cohort performed with an accuracy for categorical assignment of 100% (bone phenotyping) and a correlation coefficient of 0.99 (µFRAC), whereas the second-generation reproducibility cohort performed with an accuracy of 96.4% (bone phenotyping) and a correlation coefficient of 0.99 (µFRAC). CONCLUSION: We demonstrated that bone microarchitecture models trained using first-generation scan data generalize well to second-generation scans, performing with a high level of accuracy and reproducibility. Less than 4% of individuals' estimated fracture risk led to a change in treatment threshold, and in general, these dissimilar outcomes using second-generation data tended to be more conservative.

Establishing the universality of first-generation-trained HR-pQCT prediction models on second-generation scan data is important to move the bone microarchitecture field forward. We found that despite the difference in resolutions between the two HR-pQCT generations, models developed with first-generation data generalized well to second-generation systems. This avoids unnecessarily repeating complex studies.

Statistical investigation of interdependence of trabecular microarchitectural parameters from micro-computed tomography.

Standard microarchitectural analysis of bone using micro-computed tomography produces a large number of parameters that quantify the structure of the trabecular network. Analyses that perform statistical tests on many parameters are at elevated risk of making Type I errors. However, when multiple testing correction procedures are applied, the risk of Type II errors is elevated if the parameters being tested are strongly correlated. In this article, we argue that four commonly used trabecular microarchitectural parameters (thickness, separation, number, and bone volume fraction) are interdependent and describe only two independent properties of the trabecular network. We first derive theoretical relationships between the parameters based on their geometric definitions. Then, we analyze these relationships with an aggregated in vivo dataset with 2987 images from 1434 participants and a synthetically generated dataset with 144 images using principal component analysis (PCA) and linear regression analysis. With PCA, when trabecular thickness, separation, number, and bone volume fraction are combined, we find that 92 % to 97 % of the total variance in the data is explained by the first two principal components. With linear regressions, we find high coefficients of determination (0.827-0.994) and fitted coefficients within expected ranges. These findings suggest that to maximize statistical power in future studies, only two of trabecular thickness, separation, number and bone volume fraction should be used for statistical testing.