Poster — Thur Eve — 08: Rotational errors with on‐board cone beam computed tomography
Bibliographic record
Abstract
The focus of this study is on the Elekta XVI on‐board cone beam computed tomography (CBCT) system. A rotational mismatch as large as 0.5° is observed between clockwise (CW) and counter‐clockwise (CCW) CBCT scans. The error could affect non‐isocentric treatments (e.g., lung SBRT and acoustic neuroma), as well as off‐axis organs‐at‐risk. The error is caused by mislabeling of the projections with a lagging gantry angle, which is caused by the finite image acquisition time and delays in the imaging system. A 30 cm diameter cylindrical phantom with 5 mm diameter holes is used for the scanning. CW and CCW scans are acquired for five gantry speeds (360 to 120 deg./min.) on six linacs from three generations (MLCi, MLCi2, and Agility). Additional scans are acquired with different x‐ray pulse widths for the same mAs. In the automated CBCT analysis (using ImageJ), the CW/CCW mismatch in a series of line profiles is identified and used to calculate the rotational error. Results are consistent among all linacs and indicate that the error varies linearly with gantry speed. The finite width of the x‐ray pulses is a major but predictable contributor to the delay causing the error. For 40 ms pulses, the delay is 34 ± 1 ms. A simple solution applied in our clinic is adjusting the gantry angle offset to make the CCW one‐minute scans correct. A more involved approach we are currently investigating includes adjustments of pulse width and mA, resulting in focal spot changes, with potential impact on image quality.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".