Improving soft tissue imaging with volume-of-interest cone-beam CT
Bibliographic record
Abstract
Current cone-beam CT systems acquire full field-of-view projections in which x-ray scatter degrades the contrast of soft-tissue in the reconstructed images. The objective of this work was to simulate volume-of-interest (VOI) imaging, which reduces scatter and dose to the patient through beam collimation, to investigate the improvements in soft-tissue visibility on the Gamma Knife Icon. The results indicated that as field size decreased, contrast and noise increased, leading to only modest improvements in the contrast-to-noise ratio when using the same initial photon fluence. A reconstruction framework called the interior virtual method was adapted to suppress truncation-induced artifacts and noise in the VOI image. In this framework the projection data were extrapolated using a cosine function, an intermediate image was reconstructed analytically, and virtual projections of the intermediate image were created for iterative reconstruction. The framework supports high quality VOI reconstruction and can allow clinicians to optimize dose for soft-tissue visualization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".