Tomographic reconstruction strategies for nondestructive testing with a commercial CT scanner
Bibliographic record
Abstract
In this work, a framework was developed to access and process raw data from a commercial X-ray Computed Tomography (CT) scanner for research purposes. Our method requires vendor-provided binaries to convert the data to a readable format and also to remove the effect of proprietary beam hardening preprocessing. As a result, custom reconstruction techniques, including beam-hardening corrections algorithms, can be applied. Small region-of-interest CT imaging techniques and different backprojection algorithms were investigated to improve image quality (spatial resolution, noise) with an in-house iterative reconstruction algorithm. For a reconstruction matrix of 512 pixels × 512 pixels, processing times of approximately 2.5 s per slice were obtained using a set of 8 x GPUs. With this framework, high-quality images of high density samples (e.g., minerals) can be obtained with reduced truncation-induced blurring, free of artifacts stemming from the reconstruction process and reduced beam-hardening artifacts.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".