Region‐of‐interest intra‐arc MV imaging to facilitate sub‐mm positional accuracy with minimal imaging dose during treatment deliveries of small cranial lesions
Bibliographic record
Abstract
Abstract Purpose To automate the generation of region‐of‐interest (ROI) apertures for use with megavoltage imaging for online positional corrections during cranial stereotactic radiosurgery. Materials and methods Digitally reconstructed radiographs (DRRs) were created for a 3D‐printed skull phantom at 5 degree gantry angle increments for a three‐arc beam arrangement. At each angle, 3000 random rectangular apertures were generated, and 100 shifts on a grid were applied to the anatomy within the frame. For all shifts, the mutual information (MI) between the shifted and unshifted DRR was calculated to derive an average MI gradient. The top 10% of apertures that minimized registration errors were overlaid and discretely thresholded to generate imaging plans. Imaging was acquired with the skull while implementing simulated patient motion on a linac. Control point‐specific couch motions were derived to align the skull to its planned positioning. Results Apertures with a range of repositioning errors less than 0.1 mm possessed a 42% larger average MI gradient when compared with apertures with a range greater than 1 mm. Dose calculations with Monte Carlo exhibited an 84% reduction in the dose received by 50% of the skull with the 50% thresholded plan when compared to a constant 22 × 22 cm2 imaging plan. For all different imaging plans (with and without motion), the calculated median 3D‐errors with respect to the tracking of a metal‐BB fiducial positioned at isocenter in the skull were sub‐mm except for the 80% thresholded plan. Conclusions Sub‐mm positional errors are achievable with couch motions derived from control point–specific ROI imaging. Smaller apertures that conform to an anatomical ROI can be utilized to minimize the imaging dose incurred at the expense of larger errors.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".