Interpolation of CT Projections by Exploiting Their Self-Similarity and\n Smoothness
Bibliographic record
Abstract
As the medical usage of computed tomography (CT) continues to grow, the\nradiation dose should remain at a low level to reduce the health risks.\nTherefore, there is an increasing need for algorithms that can reconstruct\nhigh-quality images from low-dose scans. In this regard, most of the recent\nstudies have focused on iterative reconstruction algorithms, and little\nattention has been paid to restoration of the projection measurements, i.e.,\nthe sinogram. In this paper, we propose a novel sinogram interpolation\nalgorithm. The proposed algorithm exploits the self-similarity and smoothness\nof the sinogram. Sinogram self-similarity is modeled in terms of the similarity\nof small blocks extracted from stacked projections. The smoothness is modeled\nvia second-order total variation. Experiments with simulated and real CT data\nshow that sinogram interpolation with the proposed algorithm leads to a\nsubstantial improvement in the quality of the reconstructed image, especially\non low-dose scans. The proposed method can result in a significant reduction in\nthe number of projection measurements. This will reduce the radiation dose and\nalso the amount of data that need to be stored or transmitted, if the\nreconstruction is to be performed in a remote site.\n
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".