Bibliographic record
Abstract
Purpose: To develop a time efficient IMRT delivery platform that simultaneously exploits all mechanical degrees of freedom of the linac. Method and Materials: Trajectory Based Radiation Therapy (TBRT) is a new technique for planning and delivering optimized dose distributions where the radiation source moves along a continuous 3‐dimensional trajectory defined by gantry angle, couch angle, and couch position. The trajectory is constructed using a series of control points distributed along the trajectory. For planning, continuous source motion is modeled as a series of static beams with one beam defined at each control point. Highly restrictive constraints are placed on MLC and source motion to preserve a continuous, efficient and accurate delivery. Normally these restrictions would also severely limit the ability of the optimization algorithm to derive a high quality plan. This problem is solved using a novel technique for aperture based optimization where a coarse sampling of unrestricted control points is used in the initial stages of optimization. As the optimization progresses additional control points are added with increasing restrictions on MLC and source motion. This approach maintains time efficiency and delivery accuracy while allowing the optimization to derive a high quality plan. Results: Time studies have shown that TBRT delivery times are reduced to ∼ 1.5 to 3 minutes for a 200 cGy fraction. Thus far, results have shown that treatment plans generated with TBRT have dose distributions that are equivalent to or superior to static gantry IMRT. Conclusion: On‐line imaging techniques have provided clinicians with tools for verifying patient position and adapting treatment plans but at the expense of increased treatment time. TBRT is well suited for on‐line verification and adaptation with delivery times that are substantially shorter than static gantry IMRT, IMAT and Tomotherapy. Conflict of Interest: Supported in part by Varian Medical Systems.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".