The influence of antiscatter grids on soft‐tissue detectability in cone‐beam computed tomography with flat‐panel detectors
Bibliographic record
Abstract
The influence of antiscatter x-ray grids on image quality in cone-beam computed tomography (CT) is evaluated through broad experimental investigation for various anatomical sites (head and body), scatter conditions (scatter-to-primary ratio (SPR) ranging from approximately 10% to 150%), patient dose, and spatial resolution in three-dimensional reconstructions. Studies involved linear grids in combination with a flat-panel imager on a system for kilovoltage cone-beam CT imaging and guidance of radiation therapy. Grids were found to be effective in reducing x-ray scatter "cupping" artifacts, with heavier grids providing increased image uniformity. The system was highly robust against ring artifacts that might arise in CT reconstructions as a result of gridline shadows in the projection data. The influence of grids on soft-tissue detectability was evaluated quantitatively in terms of absolute contrast, voxel noise, and contrast-to-noise ratio (CNR) in cone-beam CT reconstructions of 16 cm "head" and 32 cm "body" cylindrical phantoms. Imaging performance was investigated qualitatively in observer preference tests based on patient images (pelvis, abdomen, and head-and-neck sites) acquired with and without antiscatter grids. The results suggest that although grids reduce scatter artifacts and improve subject contrast, there is little strong motivation for the use of grids in cone-beam CT in terms of CNR and overall image quality under most circumstances. The results highlight the tradeoffs in contrast and noise imparted by grids, showing improved image quality with grids only under specific conditions of high x-ray scatter (SPR> 100%), high imaging dose (Dcenter> 2 cGy), and low spatial resolution (voxel size > or = 1 mm).
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".