SU‐FF‐I‐45: An Automatic Method for Reduction of Metal Artifacts Caused by Metallic Implants
Bibliographic record
Abstract
Purpose: To develop an automatic method for metal artifact reduction (MAR) from small objects such as brachytherapy (BT) seeds. Method and Materials: A phantom made of agar (water‐like) and consisting of 6 slices of 5 mm in which 75 seeds (activity at background level) were implanted for imaging purposes. The phantom was scanned on a helical CT scanner (Siemens Somatom) to produce continuous 1 mm and 3 mm slices of the full phantom. The proposed method is based on the interpolation of missing projections by directly using raw CT data (sinogram). First, an initial image was reconstructed from the raw projection data. Then, the metal objects segmented from the reconstructed image were re‐projected into the same sinogram. The Steger method was used to precisely determine the position and edges of the seed traces in raw CT data. By combining the use of Steger detection and re‐projections, the missing projections were finally detected and further replaced by interpolation of non‐missing neighbouring projections. Results: In both phantom experiment and patient studies, the missing projections have been well detected and the artifacts caused by metallic objects in the image reconstructed using the corrected sinogram have been significantly reduced. The performance of the algorithm has been also proven after a quantitative evaluation by comparing the intensity uniformity between the uncorrected and corrected phantom images. Conclusion: An efficient algorithm for MAR in seed brachytherapy was developed. The challenge of detecting and correcting artifacts from 60 to 120 tiny objects in sinogram space has been successfully demonstrated. The detected traces can be further processed to extract, from a large sample of points, the position and orientation of each seed with high precision. This should enable a more accurate use of advance brachytherapy dose calculations, such as Monte Carlo simulations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".