Quantification of bone mineral, collagen, and water using a robust DECT-based algorithm: addressing attenuation similarity and CT imaging noise
Bibliographic record
Abstract
Bone consists of hydroxyapatite, collagen, and water, each essential to mechanical integrity. Dual-energy computed tomography (DECT) offers a non-invasive way to quantify these components, but attenuation similarity between collagen and water and CT noise undermine stable decomposition. This study develops and evaluates a robust, constraint-based DECT algorithm to improve stability and accuracy under these conditions. The method was first verified using digital CT phantoms with prescribed compositions and then validated using 28 cylindrical bovine specimens scanned at 45/90 keV. Stepwise drying (110 °C, 6h) and ashing (600 °C, 9h) provided reference fractions of hydroxyapatite, collagen, and water. Simulations demonstrated high voxel-wise accuracy with negligible deviation from reference values. Experimentally, DECT-derived and ashing-measured fractions correlated moderately for hydroxyapatite (r = 0.61, p < 0.001) and collagen (r = 0.46, p = 0.02), but poorly for water (r = 0.02, p = 0.91), reflecting attenuation similarity and dehydration-related bonded-water loss. After excluding seven specimens with severe beam-hardening and streak artefacts, hydroxyapatite accuracy improved markedly (r = 0.89, p < 0.001). The algorithm enhances the reliability of DECT-based bone-composition assessment under realistic noise, providing robust hydroxyapatite quantification. Collagen–water separation remains limited, and future work will integrate advanced denoising and multi-energy CT.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".