Feasibility study of multi-pass respiratory-gated helical tomotherapy of a moving target via binary MLC closure
Bibliographic record
Abstract
Gated radiotherapy of lung lesions is particularly complex for helical tomotherapy, due to the simultaneous motions of its three subsystems (gantry, couch and collimator). We propose a new way to implement gating for helical tomotherapy, namely multi-pass respiratory gating. In this method, gating is achieved by delivering only the beam projections that occur within a respiratory gating window, while blocking the rest of the beam projections by fully closing all collimator leaves. Due to the continuous couch motion, the planned beam projections must be delivered over multiple passes of radiation deliveries. After each pass, the patient couch is reset to its starting position, and the treatment recommences at a different phase of tumour motion to 'fill in' the previously blocked beam projections. The gating process may be repeated until the plan dose is delivered (full gating), or halted after a certain number of passes, with the entire remaining dose delivered in a final pass without gating (partial gating). The feasibility of the full gating approach was first tested for sinusoidal target motion, through experimental measurements with film and computer simulation. The optimal gating parameters for full and partial gating methods were then determined for various fractionation schemes through computer simulation, using a patient respiratory waveform. For sinusoidal motion, the PTV dose deviations of -29 to 5% observed without gating were reduced to range from -1 to 3% for a single fraction, with a 4 pass full gating. For a patient waveform, partial gating required fewer passes than full gating for all fractionation schemes. For a single fraction, the maximum allowed residual motion was only 4 mm, requiring large numbers of passes for both full (12) and partial (7 + 1) gating methods. The number of required passes decreased significantly for 3 and 30 fractions, allowing residual motion up to 7 mm. Overall, the multi-pass gating technique was shown to be a promising way to reduce the impact of lung tumour motion during helical tomotherapy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".