An improved method for the removal of ring artifacts in high resolution CT imaging
Bibliographic record
Abstract
Abstract In high resolution computed tomography (CT) using flat panel detectors, imperfect or defected detector elements cause stripe artifacts in sinogram which results in concentric ring artifacts in the image. Such ring artifacts obscure image details in the regions of interest of the image. In this article, novel techniques are proposed for the detection, classification, and correction of ring artifacts in the sinogram domain. The proposed method is suitable for multislice CT with parallel or fan beam geometry. It can also be employed for ring artifact removal in 3D cone beam volume CT by adopting a sinogram by sinogram processing technique. The detection algorithm is based on applying data driven thresholds on the mean curve and difference curve of the sinogram. The ring artifacts are classified into three types and a separate correction algorithm is used for each class. The performance of the proposed techniques is evaluated on a number of real micro-CT images. Experimental results corroborate that the proposed algorithm can remove ring artifacts from micro-CT images more effectively as compared to other recently reported techniques in the literature.
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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 | 0.001 |
| 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 | 0.001 |
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".