An empirical method for lag correction in cone‐beam CT
Bibliographic record
Abstract
Image lag degrades image quality in cone-beam CT (CBCT), resulting in contrast reduction, lack of CT number accuracy and uniformity, and skin-line artifacts. The magnitude of such degradation and the extent to which imaging performance can be improved by means of a lag correction method were investigated. Measurements were performed using a radiotherapy CBCT guidance system (Elekta Synergy XVI, Elekta Oncology Systems, Atlanta, GA), for which the imaging system is based upon a RID1640-AL1 flat-panel imager (Perkin Elmer, Wiesbaden, Germany). Image lag and its relationship to various parameters including signal magnitude, photon energy, and frame number were investigated, and an empirical lag correction method was developed to manage lag artifacts. The correction method was simply the subtraction from the current frame by previous frames weighted by the temporal response function. The CatPhan 500 phantom (The Phantom Laboratory, Salem, NY) within an irregularly shaped body annulus was used to demonstrate the magnitude of artifacts with and without lag correction. CBCT images after correction demonstrated improvement in skin-line reconstruction, CT number accuracy, image uniformity, and contrast-to-noise ratio. Lag artifacts can be reduced by means of algorithmic correction of the projection images. Lag correction is most important for all shapes of objects having contrast inserts. For circular/cylindrical objects, lag correction does not improve the skin-line artifact but can improve low contrast visibility adjacent to high contrast objects.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".