Technical note: Sinogram merging to compensate for truncation of projection data in tomotherapy imaging
Bibliographic record
Abstract
An advantage of helical tomotherapy radiation therapy systems is that on-line megavoltage computed tomography (CT) images can be reconstructed to verify patient positioning. One limitation of such systems is that the field-of-view (FOV) of the photon fan-beam is limited by the aperture size of the binary multileaf collimator (MLC) used to modulate treatment beams. For patients larger than the FOV the acquired sinograms will be truncated causing artifacts in the resultant megavoltage CT images. Computer simulations are used to demonstrate that such artifacts can be eliminated or at least reduced by merging appropriately acquired truncated fan-beam sinograms to form a nontruncated parallel-beam sinogram. The necessary fan-beam sinograms are acquired with the patient translated to different offset locations within the gantry. The parallel-beam sinogram is then used to reconstruct the final CT image. The increase in patient dose due to the acquisition of more than one fan-beam sinogram can be reduced by using properly designed binary MLC fields to block redundant projection rays.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.003 |
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".