Resolution enhancement in digital x-ray imaging
Bibliographic record
Abstract
We have developed a restoration method for radiographs that enhances image sharpness and reveals bone microstructures that were initially hidden in the soft-tissue glare. The method is two fold: the image is first deconvolved using the Richardson-Lucy algorithm and is then divided with a signal modelling the soft-tissue distribution to increase the overall contrast. Each step has its own merits but the power of the restoration method lies in their combination. The originality of the method is its reliance on a priori information at each step in the processing. We have measured and modelled analytically the point-spread function of a low-dose gas microstrip x-ray detector at several beam energies. We measured the relationship between the local image intensity and the noise variance for these images. The soft-tissue signal was also modelled using a minimum-curvature filtering technique. These results were then combined into an image deconvolution procedure that uses wavelet filtering to reduce restoration noise while keeping the enhanced small-scale features. The method was applied successfully to images of a human-torso phantom and improved the contrast of small details on the bones and in the soft tissues. We measured a mean 54% increase in signal to noise ratio and a mean 105% increase in contrast to noise ratio in the 70 and 140 kVp images we analysed. The method was designed to facilitate the analysis of radiographs by relying on two levels of visual inspection. The contrast of the full image is first enhanced by division with the signal modelling the soft-tissue distribution. Based on the result, a radiologist might decide to zoom in on a given image section. The full restoration method is then applied to that region of interest. Indeed, full image deconvolution is often unnecessary since enhanced small-scale details are not visible at large scale; only the section of interest is processed which is more efficient.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".