An x-ray CT polymer gel dosimetry prototype: I. Remnant artefact removal
Bibliographic record
Abstract
In this study a new x-ray CT polymer gel dosimetry (PGD) filtering technique is presented for the removal of (i) remnant ring and streak artefacts, and (ii) 'structured' noise in the form of minute, intrinsic gel density fluctuations. It is shown that the noise present within x-ray CT PGD images is not purely stochastic (pixel by pixel) in nature, but rather is 'structured', and hence purely stochastic-based noise-removal filters fail in removing this significant, unwanted noise component. The remnant artefact removal (RAR) technique is based on a class of signal stripping (i.e. baseline-estimation) algorithms typically used in the estimation of unwanted non-uniform baselines underlying spectral data. Here the traditional signal removal algorithm is recast, whereby the 'signal' that is removed is the structured noise and remnant artefacts, leaving the desired polymer gel dose distribution. The algorithm is extended to 2D and input parameters are optimized for PGD images. RAR filter results are tested on (i) synthetic images with measured gel background images added, in order to accurately represent actual noise present in PGD images, and (ii) PGD images of a three-field gel irradiation. RAR results are compared to a top-performing noise filter (adaptive mean, AM), used in previous x-ray CT PGD studies. It is shown that, in all cases, the RAR filter outperforms the AM filter, particularly in cases where either (i) a low-dose gel image has been acquired or (ii) the signal-to-noise ratio of the PG image is low, as in the case when a low number of image averages are acquired within a given experiment. Guidelines for the implementation of the RAR filter are given.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".