Investigation of the dosimetric effect of respiratory motion using four-dimensional weighted radiotherapy
Bibliographic record
Abstract
We have developed a four-dimensional weighted radiotherapy (4DW-RT) technique. This method involves designing the motion of the linear accelerator beam to coincide with the tumour motion determined from 4D-CT imaging while including a weighting factor to account for irregular motion and limitations of the delivery system. Experiments were conducted with a moving phantom to assess limitations of the delivery system when applying this method. Although the multi-leaf collimator motion remains within the tolerance of the linear accelerator, the extent of motion was less than 1 mm larger than the designed one, and there was a net system latency of approximately 0.2 s. The dose distributions were measured and simulated using different weighting factors and motion scenarios. The breathing characteristics (period, extent of motion, drift and standard deviations) of 32 patients were evaluated using the Varian RPM system. Breathing variability was assessed by plotting the average breathing motion as a function of the breathing phase. Simulations were carried out to determine the optimal weighting factor based on typical patient breathing characteristics. These results establish that the 4DW-RT method demonstrates potential for dose escalation without increasing exposure to healthy tissue.
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 | 0.001 | 0.003 |
| 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) | 0.000 | 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".