Methods for Post Hoc Quantitative Computed Tomography Bone Density Calibration: Phantom-Only and Regression
Bibliographic record
Abstract
Quantitative computed tomography (qCT) relies on calibrated bone mineral density data. If a calibration phantom is absent from the CT scan, post hoc calibration becomes necessary. Scanning a calibration phantom after-the-fact and applying that calibration to uncalibrated scans has been used previously. Alternatively, the estimated density is known to vary with CT settings, suggesting that it may be possible to predict the calibration terms using CT settings. This study compares a novel CT setting regression method for post hoc calibration to standard and post hoc phantom-only calibrations. Five cadaveric upper limbs were scanned at 11 combinations of peak tube voltage and current (80-140 kV and 100-300 mA) with two calibration phantoms. Density calibrations were performed for the cadaver scans, and scans of the phantoms alone. Stepwise linear regression determined if the calibration equation terms were predictable using peak tube voltage and current. Peak tube voltage, but not current, was significantly correlated with regression calibration terms. Calibration equation slope was significantly related to the type of phantom (p < 0.001), calibration method (p = 0.026), and peak tube voltage (p < 0.001), but not current (p = 1.000). The calibration equation vertical intercept was significantly related to the type of phantom (p < 0.001), and peak tube voltage (p = 0.006), but not calibration method (p = 0.682), or current (p = 0.822). Accordingly, regression can correlate peak tube voltage with density calibration terms. Suggesting that, while standard qCT calibration is preferable, regression calibration may be an acceptable post hoc method when necessary.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".