The stability of mechanical calibration for a kV cone beam computed tomography system integrated with linear acceleratora)
Bibliographic record
Abstract
The geometric accuracy and precision of an image-guided treatment system were assessed. Image guidance is performed using an x-ray volume imaging (XVI) system integrated with a linear accelerator and treatment planning system. Using an amorphous silicon detector and x-ray tube, volumetric computed tomography images are reconstructed from kilovoltage radiographs by filtered backprojection. Image fusion and assessment of geometric targeting are supported by the treatment planning system. To assess the limiting accuracy and precision of image-guided treatment delivery, a rigid spherical target embedded in an opaque phantom was subjected to 21 treatment sessions over a three-month period. For each session, a volumetric data set was acquired and loaded directly into an active treatment planning session. Image fusion was used to ascertain the couch correction required to position the target at the prescribed iso-center. Corrections were validated independently using megavoltage electronic portal imaging to record the target position with respect to symmetric treatment beam apertures. An initial calibration cycle followed by repeated image-guidance sessions demonstrated the XVI system could be used to relocate an unambiguous object to within less than 1 mm of the prescribed location. Treatment could then proceed within the mechanical accuracy and precision of the delivery system. The calibration procedure maintained excellent spatial resolution and delivery precision over the duration of this study, while the linear accelerator was in routine clinical use. Based on these results, the mechanical accuracy and precision of the system are ideal for supporting high-precision localization and treatment of soft-tissue targets.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".