Markov-chain Monte Carlo-based image reconstruction for streak artefact reduction on contrast-enhanced computed tomography
Bibliographic record
Abstract
Intervertebral disc herniation is a very common disorder and contrast-enhanced computed tomography (CECT) is one of the imaging modalities for studying the causes of intervertebral disc herniation and its potential link as a mechanical source of pain. However, streak artefacts caused by the contrast agent reduce the quality of the reconstructed image. We therefore propose a novel image reconstruction technique for reducing streak artefacts in CECT images of the intervertebral disc. The technique identifies the contrast agent-affected region in projection space using a multi-scale segmentation algorithm, which is followed by reconstruction via Markov-chain Monte Carlo estimation. The results were compared with two existing artefact-reducing techniques (non-iterative and iterative), and the proposed method showed an improvement on signal-to-noise ratio (53.1 dB) while non-iterative and iterative approaches yielded 26.5 and 48.4 dB, respectively. The proposed image reconstruction technique can reduce streak artefacts on CECT images of intervertebral disc herniation and it can be extended to other streak artefacts caused by the contrast agent on computed tomography images.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".