Po‐Poster ‐ 06: Beam spot motion of medical linear accelerators
Bibliographic record
Abstract
Megavoltage cone‐beam computed tomography (MV‐CBCT) is a volumetric imaging method that can improve patient setup verification techniques. MV‐CBCT utilizes the treatment beam to obtain projections at every 1–2° around the patient. For this to be clinically acceptable, total dose received by the patient from all imaging sessions must be kept to a minimum and typically should be ⩽5% of the prescribed dose. This necessitates the use of extremely low doses (<<1MU) in the acquisition of each projection. At such low dose levels beam spot instability is known to exist and can compromise image quality. The purpose of this work is to quantify the beam spot motion of Siemen's accelerator for a conventional 6 MV beam and 5.4 MV “imaging beam” generated with a beryllium target. This was accomplished by using an a‐Si flat panel detector to image a cone‐beam geometric calibration phantom and using a calibration algorithm to derive the spot motion in reference frames fixed in space and/or attached to the gantry. Motion of the beam spot was observed immediately after beam startup primarily in the gun‐target direction. The maximum fixed motion of the 6MV beam spot was 1.1±0.3 mm and is similar to that observed for the low Z beam (1.2±0.1 mm). However, the beam spot position of the latter stabilized at about 0.5 MU compared to 6 MU for the 6 MV beam and had much less fluctuations once stabilized. The beam spot position of the conventional beam was much less reproducible than the low Z beam.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".