Correlation Between Cone-Beam Computed Tomography and High-Resolution Peripheral Computed Tomography for Assessment of Wrist Bone Microstructure
Bibliographic record
Abstract
ABSTRACT High-resolution peripheral quantitative computed tomography (HR-pQCT) is considered as the best technique to measure bone microarchitecture in vivo. However, a breakthrough for medical applications is inhibited because of the restricted field of view (∼9 mm) and a relatively long acquisition time (∼3 minutes). The goal of this study was to compare the accuracy of cone-beam computed tomography (CBCT) and HR-pQCT and to determine the agreement between CBCT and HR-pQCT in quantifying bone structural parameters. Nineteen trapezia of arthritic patients were scanned four times ex vivo: 1) CBCT (NewTom 5G, Cefla, at 75 μm); 2) HR-pQCT (XTremeCT-I, Scanco, at 82 μm); 3) HR-pQCT (XTremeCT-II, Scanco, at 60.7 μm); and 4) microCT (SkyScan1172, Bruker, at 19.84 μm). XTremeCT-I and XtremeCT-II were reconstructed, segmented, and analyzed following the manufacturer's guidelines. CBCT was reconstructed with in-house developed software and analyzed twice: once with an adaptive segmentation technique combined with a direct analysis method (AT-DM) and once with a Laplace-Hamming filtering technique combined with an indirect analysis method (LH-IM). Parameters of interest included bone volume fraction (BV/TV) and trabecular thickness (Tb.Th), separation (Tb.Sp), and number (Tb.N). The analyses of the CBCT data showed that the AT-DM analysis correlated better with microCT for BV/TV, Tb.Sp, and Tb.N, whereas the LH-IM technique correlated better for Tb.Th. Evaluated over all parameters, the coefficient of determination for XtremeCT-I, XtremeCT-II, and CBCT were higher as R2 = 0.68, 0.72, and 0.67, respectively. For CBCT, the correlations improved when three samples with very thin trabeculae close to each other were excluded and became similar to those for XtremeCT-I and XtremeCT-II. Interesting for clinical practice is that those bones could be identified automatically with the CBCT scanner. We conclude that CBCT produced similar accuracy as HR-pQCT in bone morphometric analyses of trapezia. The broader range of application, larger field of view, and shorter acquisition time make CBCT a valuable alternative to HR-pQCT. © 2019 American Society for Bone and Mineral Research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".