Effect of object location on the density measurement and Hounsfield conversion in a NewTom 3G cone beam computed tomography unit
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to determine the effect of an object's location in a cone beam CT imaging chamber (CBCT-NewTom 3G) on its apparent density and to develop a linear conversion coefficient for Hounsfield units (HU) to material density (g cm(-3)) for the NewTom 3G Scanner. METHODS: Three cylindrical models of materials with different densities were constructed and scanned at five different locations in a NewTom 3G Volume Scanner. The average HU value for each model at each location was obtained using two different types of software. Next, five cylinders of different known densities were scanned at the exact centre of a NewTom 3G Scanner. The collected data were analysed using the same two types of software to determine a standard linear relationship between density and HU for each type of software. RESULTS: There is no statistical significance of location of an object within the CBCT scanner on determination of its density. A linear relationship between the density of an object and the HU of a scan was rho = 0.001(HU)+1.19 with an R2 value of 0.893 (where density, rho, is measured in g cm(-3)). This equation is to be used on a range between 1.42 g cm(-3) and 0.4456 g cm(-3). CONCLUSIONS: A linear relationship can be used to determine the density of materials (in the density range of bone) from the HU values of a CBCT scan. This relationship is not affected by the object's location within the scanner itself.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".