Material‐specific analysis using coherent‐scatter imaging
Bibliographic record
Abstract
Coherent-scatter computed tomography (CSCT) is a novel imaging method we are developing to produce cross-sectional images based on the low-angle (<10 degrees) scatter properties of tissue. At diagnostic energies, this scatter is primarily coherent with properties dependent upon the molecular structure of the scatterer. This facilitates the production of material-specific maps of each component in a conglomerate. Our particular goal is to obtain quantitative maps of bone-mineral content. A diagnostic x-ray source and image intensifier are used to acquire scatter patterns under first-generation CT geometry. An accurate measurement of the scatter patterns is necessary to correctly identify and quantify tissue composition. This requires corrections for exposure fluctuations, temporal lag in the intensifier, and self-attenuation within the specimen. The effect of lag is corrected using an approximate convolution method. Self-attenuation causes a cupping artifact in the CSCT images and is corrected using measurements of the transmitted primary beam. An accurate correction is required for reliable density measurements from material-specific images. The correction is shown to introduce negligible noise to the images and a theoretical expression for CSCT image SNR is confirmed by experiment. With these corrections, the scatter intensity is proportional to the number of scattering centers interrogated and quantitative measurements of each material (in g/cm3) are obtained. Results are demonstrated using both a series of poly(methyl methacrylate) (PMMA) sheets of increasing thickness (2-12 mm) and a series of 5 acrylic rods containing varying amounts of hydroxyapatite (0-0.400 g/cm3), simulating the physiological range of bone-mineral density (BMD) found in trabecular bone. The excellent agreement between known and measured BMD demonstrates the viability of CSCT as a tool for densitometry.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".