Extending the dynamic range of biomedical micro-computed tomography for application to geomaterials
Bibliographic record
Abstract
BACKGROUND: X-ray computed tomography (CT) can non-destructively examine objects by producing three-dimensional images of their internal structure. Although the availability of biomedical micro-CT offers the increased access to scanners, CT images of dense objects are susceptible to artifacts particularly due to beam hardening. OBJECTIVE: This study proposes and evaluates a simple semi-empirical correction method for beam hardening and scatter that can be applied to biomedical scanners. METHODS: Novel calibration phantoms of varying diameters were designed and built from aluminum and poly[methyl-methacrylate]. They were imaged using two biomedical micro-CT scanners. Absorbance measurements made through different phantom sections were fit to polynomial and inversely exponential functions and used to determine linearization parameters. Corrections based on the linearization equations were applied to the projection data before reconstruction. RESULTS: Correction for beam hardening was achieved when applying both scanners with the correction methods to all test objects. Among them, applying polynomial correction method based on the aluminum phantom provided the best improvement. Correction of sample data demonstrated a high agreement of percent-volume composition of dense metallic inclusions between using the Bassikounou meteorite from the micro-CT images (13.7%) and previously published results using the petrographic thin sections (14.6% 8% metal and 6.6% troilite). CONCLUSIONS: Semi-empirical linearization of X-ray projection data with custom calibration phantoms allows accurate measurements to be obtained on the radiodense samples after applying the proposed correction method on biomedical micro-CT images.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".