Bone‐composition imaging using coherent‐scatter computed tomography: Assessing bone health beyond bone mineral density
Bibliographic record
Abstract
Quantitative analysis of bone composition is necessary for the accurate diagnosis and monitoring of metabolic bone diseases. Accurate assessment of the bone mineralization state is the first requirement for a comprehensive analysis. In diagnostic imaging, x-ray coherent scatter depends upon the molecular structure of tissues. Coherent-scatter computed tomography (CSCT) exploits this feature to identify tissue types in composite biological specimens. We have used CSCT to map the distributions of tissues relevant to bone disease (fat, soft tissue, collagen, and mineral) within bone-tissue phantoms and an excised cadaveric bone sample. Using a purpose-built scanner, we have measured hydroxyapatite (bone mineral) concentrations based on coherent-scatter patterns from a series of samples with varying hydroxyapatite content. The measured scatter intensity is proportional to mineral density in true g/cm3. Repeated measurements of the hydroxyapatite concentration in each sample were within, at most, 2% of each other, revealing an excellent precision in determining hydroxyapatite concentration. All measurements were also found to be accurate to within 3% of the known values. Phantoms simulating normal, over-, and under-mineralized bone were created by mixing known masses of pure collagen and hydroxyapatite. An analysis of the composite scatter patterns gave the density of each material. For each composite, the densities were within 2% of the known values. Collagen and hydroxyapatite concentrations were also examined in a bone-mimicking phantom, incorporating other bone constituents (fat, soft tissue). Tomographic maps of the coherent-scatter properties of each specimen were reconstructed, from which material-specific images were generated. Each tissue was clearly distinguished and the collagen-mineral ratio determined from this phantom was also within 2% of the known value. Existing bone analysis techniques cannot determine the collagen-mineral ratio in intact specimens. Finally, to demonstrate the in situ potential of this technique, the mineralization state of an excised normal cadaveric radius was examined. The average collagen-mineral ratio of the cortical bone derived from material-specific images of the radius was 0.53+/-0.04, which is in agreement with the expected value of 0.55 for healthy bones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".