The effects of field-of-view and patient size on CT numbers from cone-beam computed tomography
Bibliographic record
Abstract
Cone-beam computed tomography (CBCT) is used for patient alignment before treatment and is ideal for use in adaptive radiotherapy to account for tumor shrinkage, organ deformation and weight loss. However, CBCT images are prone to artifacts such as streaking and cupping effects, reducing image quality and CT number accuracy. Our goal was to determine the optimum combination of cone-beam imaging options to increase the accuracy of image CT numbers. Several phantoms with and without inserts of known relative electron densities were imaged using the Varian on-board imaging system. It was found that CT numbers are most influenced by the selection of field-of-view and are dependent on object size and filter type. Image acquisition in half-fan mode consistently produced more accurate CT numbers, regardless of phantom size. Values measured using full-fan mode can differ by up to 7% from planning CT values. No differences were found between CT numbers of all phantom images with low and standard dose modes.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".