Optimization of a CZT photon counting detector for contaminant detection
Bibliographic record
Abstract
Abstract In the food industry, X-ray inspection systems are utilized to ensure packaged food is free from physical contaminants to maintain a high level of food safety for consumers. However, one of the challenges in the food industry is detecting small, low-density contaminants from packaged food. Cadmium zinc telluride (CZT) photon counting detectors (PCDs) can potentially alleviate this problem given its multi-energy bin capabilities, high spatial resolution and ability to eliminate electronic noise, which is superior to the conventional energy integrating detector (EID). However, the image quality from a CZT PCD can be further improved by applying an optimized energy bin weighting scheme that maximizes energy bin images that provide the largest image contrast and lowest image noise. Therefore, in this work, five contaminant materials embedded in an acrylic phantom were imaged using a CZT PCD while the phantom was in constant motion to mimic food products moving on a conveyor belt. Energy bin optimization was performed by applying an image-based weighting scheme and these results showed contrast-to-noise ratio (CNR) improvements ranging between 1.02–1.91 relative to an equivalent EID acquisition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".