CNN-based retrieval of x-ray phase-contrast images from experimental data
Bibliographic record
Abstract
Speckle-based x-ray phase-contrast imaging (XPCI) offers enhanced sensitivity towards weakly-attenuating materials – such as human tissue – and overcomes limitations of typical phase-contrast imaging systems due to less stringent requirements for setup geometry, coherence, and propagation distances. However, obtaining high-quality phase-contrast images requires accurate tracking of the near-field speckles generated by a random diffuser. A recently proposed speckle tracking method, convolutional-neural-network-based analysis (CADE), offers superior tracking performance, reduced computational times, and robustness to noise when compared to previous state-of-the-art methods but has only been validated on simulated displacements. In this study, we validated CADE on a simulated sine wave sample and experimental XPCI images of a plastic ball. CADE achieved good accuracy in displacement estimation with a simulated sample and further successfully extracted phase contrast signals from experimental imaging data. This brings the implementation of CADE in XPCI one step closer, broadening the potential of XPCI by introducing a novel, improved speckle tracking method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".