Ring artifacts removal from synchrotron CT image slices
Bibliographic record
Abstract
Ring artifacts can occur in reconstructed images from x-ray Computerized Tomography (CT) as full or partial concentric rings superimposed on the scanned structures. Due to the data corruption by those ring artifacts in CT images, qualitative and quantitative analysis of these images are compromised. In this paper, we propose to correct the ring artifacts on the reconstructed synchrotron radiation (SR) CT image slices. The proposed correction procedure includes the following steps: (1). transform the reconstructed CT images into polar coordinates; (2) apply discrete two-dimensional (2D) wavelet transform to the polar image to decompose it into four image components: low pass band image component, as well as the components from horizontal, vertical and diagonal details bands; (3). apply 2D Fourier transform to the vertical details band image component only, since the ring artifacts become vertical lines in the polar coordinates; (4). apply Gaussian filtering in Fourier domain along the abscissa direction to suppress the vertical lines, since the information of the vertical lines in Fourier domain is completely condensed to that direction; (5). perform inverse Fourier transform to get the corrected vertical details band image component; (6). perform inverse wavelet transform to get the corrected polar image; (7). transform the corrected polar image back to Cartesian coordinates to get the CT image slice with reduced ring artifacts. This approach has been successfully used on CT data acquired from the Biomedical Imaging and Therapy (BMIT) beamline in Canadian Light Source (CLS), and the results show that the ring artifacts in original SR CT images have been effectively suppressed with all the structure information in the image preserved.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 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".