Influence of focal spot on characteristics of very small diameter radiosurgical beams
Bibliographic record
Abstract
Percentage depth dose (PDD) distributions and beam profiles of very small diameter (1.5-5 mm) megavoltage radiosurgical beams calculated with Monte Carlo (MC) technique critically depend on the diameter of the circular focal spot used in the simulation: The smaller is the field diameter, the larger is the effect. Thus, in simulations of radiosurgical fields that have diameters of the order of the focal spot size, an accurate focal spot geometry should be used. We used a simplified moving slit technique in conjunction with a diode detector for evaluation of the focal spot size and shape of a megavoltage 6 MV linac as well as for determination of the equivalent focal spot diameter of the linac for use in MC simulations. The measured total diode signal contains three components: A direct focal spot signal, a background signal, and an extra-focal radiation signal. A single profile scan of the focal spot signal is Gaussian like in shape, and its full width at half maximum is used to define the focal spot dimension for this scan. The focal spot of our 6 MV linac is approximated with a Gaussian circle, and when the geometry of the effective focal spot circle is used in MC simulations, the agreement between MC-calculated and measured PDD distributions as well as beam profiles is good even for radiosurgical fields as small as 1.5 mm in diameter. Our results also confirm that matching the penumbral areas of accurately measured large-field beam profiles to the same areas of the MC-calculated beam profiles reliably leads to a realistic effective focal spot size for use in MC simulations of very small diameter beams.
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 | 0.005 |
| 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 | 0.001 | 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".