SU-FF-T-312: Motion in Tomotherapy:Some Dosimetric Observations
Bibliographic record
Abstract
Purpose: Tomotherapy is an approach to delivering radiation where the patient is continuously transported through a modulated slit of radiation. Concern arises as to how much the patient's internal motion (e.g. breathing) affects the dose deposited in the anatomy. A simple model is presented which qualitatively explains the motion-induced dose variations which were measured from film. Method and Materials: Modeling was based on the dose delivery parameters of the commercial tomotherapy unit which was used to deliver typical patient plans. Dosimetry films were irradiated while a phantom was static, and also oscillating +/− 10mm at a typical breathing repetition period of 6 seconds. Results: Comparing the static and moving cases (for the same plan), in the high dose regions (∼250 cGy) the measured doses are very similar (typically within 7%) except for the penumbra region. These results are consistent with recently independently published data, but are much less than some previous papers had suggested. Our model proposes that the surprisingly modest dose variation can be qualitatively understood in terms of the delivery mechanism of the equipment which in this case has a maximum dose rate of 850 cGy/min at isocenter. Two factors are especially important: (1) to obtain a high dose level, a particular voxel of patient anatomy must be irradiated for a long period of time (at least ∼ 20 seconds), which intrinsically allows for significant averaging over the breathing cycle; (2) the usual CTV dose uniformity requirement encourages angular symmetry and hence, due to the gantry rotation period, more temporal averaging. Conclusion: The model shows that certain dose delivery features, and attributes of a clinical plan, are important in reducing motion-induced dose variance. A fully accurate calculation is beyond the scope of this presentation, and each clinical plan should be independently evaluated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".