Rotational artifacts in on-board cone beam computed tomography
Bibliographic record
Abstract
Rotational artifacts in image guidance systems lead to registration errors that affect non-isocentric treatments and dose to off-axis organs-at-risk. This study investigates a rotational artifact in the images acquired with the on-board cone beam computed tomography system XVI (Elekta, Stockholm, Sweden). The goals of the study are to identify the cause of the artifact, to characterize its dependence on other quantities, and to investigate possible solutions. A 30 cm diameter cylindrical phantom is used to acquire clockwise and counterclockwise scans at five speeds (120 to 360 deg min(-1)) on six Elekta linear accelerators from three generations (MLCi, MLCi2 and Agility). Additional scans are acquired with different pulse widths and focal spot sizes for the same mAs. Image quality is evaluated using a common phantom with an in-house three dimensional contrast transfer function attachment. A robust, operator-independent analysis is developed which quantifies rotational artifacts with 0.02° accuracy and imaging system delays with 3 ms accuracy. Results show that the artifact is caused by mislabelling of the projections with a lagging angle due to various imaging system delays. For the most clinically used scan speed (360 deg min(-1)), the artifact is ∼0.5°, which corresponds to ∼0.25° error per scan direction with the standard Elekta procedure for angle calibration. This leads to a 0.5 mm registration error at 11 cm off-center. The artifact increases linearly with scan speed, indicating that the system delay is independent of scan speed. For the most commonly used pulse width of 40 ms, this delay is 34 ± 1 ms, part of which is half the pulse width. Results are consistent among the three linac generations. A software solution that corrects the angles of individual projections is shown to eliminate the rotational error for all scan speeds and directions. Until such a solution is available from the manufacturer, three clinical solutions are presented, which reduce the rotational error without compromising 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".