The helical tomotherapy thread effect
Bibliographic record
Abstract
Inherent to helical tomotherapy is a dose variation pattern that manifests as a "ripple" (peak-to-trough relative to the average). This ripple is the result of helical beam junctioning, completely unique to helical tomotherapy. Pitch is defined as in helical CT, the couch travel distance for a complete gantry rotation relative to the axial beam width at the axis of rotation. Without scattering or beam divergence, an analytical posing of the problem as a simple integral predicts minima near a pitch of 1/n where n is an integer. A convolution-superposition dose calculator (TomoTherapy, Inc.) included all the physics needed to explore the ripple magnitude versus pitch and beam width. The results of the dose calculator and some benchmark measurements demonstrate that the ripple has sharp minima near p=0.86(1/n). The 0.86 factor is empirical and caused by a beam junctioning of the off-axis dose profiles which differ from the axial profiles as well as a long scatter tail of the profiles at depth. For very strong intensity modulation, the 0.86 factor may vary. The authors propose choosing particular minima pitches or using a second delivery that starts 180 deg off-phase from the first to reduce these ripples: "Double threading." For current typical pitches and beam widths, however, this effect is small and not clinically important for most situations. Certain extremely large field or high pitch cases, however, may benefit from mitigation of this effect.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".