Contaminant detection in non-destructive testing using a CZT photon-counting detector
Bibliographic record
Abstract
With recent advances in the growth of CdZnTe (CZT) sensors, high-flux photon-counting detectors (PCDs) have begun to see more use commercially in non-destructive testing (NDT). One such application is food inspection, where radiography is currently used to detect undesirable contaminants introduced in the production and packaging processes. PCDs can offer better detection than conventional radiography due to the preservation of energy data by analyzing the pulse height of each x-ray detection and sorting the x-ray into one of a number of energy bins. However, there are a number of parameters that must be explored in order to offer efficient and efficacious detection of contaminants. Here, two such parameters were investigated in a phantom study with an 8×24 mm 2 CZT detector for a number of common contaminant materials. The detectability of contaminants was evaluated based on their contrast-to-noise ratio (CNR) in 2D transmission images. First, the energy bin demonstrating the highest CNR for each contaminant material was found by adjusting the threshold energies defining the edges of the bin. Second, various pixel binning schemes were utilized to lower noise and investigate the effect on the detectability based on the size of contaminants. CNR was maximized for pixel binning that corresponded to the approximate size of the contaminant objects in x-ray 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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".