Empirical quantification of bone mineral and organic phase attenuation coefficients using CT imaging and controlled thermal processing
Bibliographic record
Abstract
Accurate quantification of bone mineral, organic, and water phases is critical for evaluating bone quality, assessing fracture risk, and diagnosing skeletal diseases. Dual-energy computed tomography (DECT) holds promise for decomposing these phases but fundamentally relies on precise linear attenuation coefficients (LACs) for each phase. Existing surrogate-based imaging methods—typically assuming fixed phase attenuation coefficients—fail to reflect the compositional heterogeneity of native bone, leading to systematic errors in decomposition. This study presents a novel empirical framework for determining the average LACs of bone mineral and organic phases using 28 standardized cylindrical bovine bone specimens. The method integrates high-resolution CT imaging with a controlled drying–ashing protocol to isolate phase-specific masses and volumes. In conjunction with a linear mixture attenuation model, specimen-wise average LACs for the bone, mineral, and organic phases were characterized at selected energy levels. Results showed the following: 1) Average LACs had wide variability across the specimens, e.g. , ranging from 1.16 to 2.38 cm −1 for mineral phase and 0.004–0.085 cm −1 for organic phase at 45 kV, highlighting the limitations of using fixed surrogate-based values. 2) Bone density correlated more strongly with organic density than with mineral density, emphasizing the importance of accurately quantifying the organic phase for bone quality and health assessment. 3) Bone LACs were strongly correlated with mineral LACs but not with organic LACs, underscoring the inability of single-energy CT to capture meaningful information from the organic phase. The proposed empirical framework demonstrates the feasibility of characterizing phase-specific LACs in real bone tissue and may support physiologically accurate DECT modeling, advancing personalized, composition-based bone diagnostics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".