X-ray imaging technique for in vitro tissue composition measurements using saline/iodine displacement
Bibliographic record
Abstract
An in vitro radiographic technique has been developed to study the composition of arterial specimens, quantifying both the calcified and soft tissue components. In the authors' new method, planar radiographs of a phantom were obtained when the phantom was immersed in: (a) a saline bath using a 45-kV, spectrum with no added filtration; and, (b) an iodine bath using a 100-kV, spectrum with 12.5 mm Al added filtration. These radiographs were then digitized and converted into bone-equivalent and Lucite images using calibration data obtained from images of Lucite and bone-equivalent step wedges. Thickness measurements from these images yielded average accuracies of /spl plusmn/300 /spl mu/m for the bone-equivalent image, and /spl plusmn/80 /spl mu/m for the Lucite image. The precision (one standard deviation) of the thickness measurements was 200 /spl mu/m and /spl plusmn/150 /spl mu/m for the bone-equivalent and the Lucite images respectively. Although the accuracy and precision of bone-equivalent thickness measurements were not as good as those obtained with dual-energy X-ray imaging, the accuracy and precision of the Lucite thickness measurements are shown to be much better. The high accuracy and precision of the Lucite thickness measurements make this technique a very good complement to dual-energy radiography (with its high accuracy and precision of bone-equivalent thickness measurement) to study the physical properties of excised arterial specimens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".