Cone beam computed tomography in 6- and 60-second acquisitions: implications for adaptive radiotherapy when respiratory motion is present
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Bibliographic record
Abstract
Abstract Purpose . To investigate the effects of respiratory motion during fast (∼6 s) and slow (∼60 s) cone beam computed tomography (CBCT) acquisition modes, with a focus on implications for adaptive radiotherapy (ART). Methods . CBCT images are compared with 4D fan beam CT acquisitions, considering average (‘AVE’) and maximum (‘MIP’) intensity projections. Data are acquired using a respiratory motion phantom representing a human thorax with a lung tumour. A range of sup-inf motion amplitudes (3 to 11 mm) and periods (3 to 5 s) are considered. HU perturbations, target contouring implications, and dosimetric effects are considered. Results . Fast mode CBCT motion artefacts are more severe for larger amplitudes and longer periods. Motion artefacts are minimal in slow mode. The standard deviation of HU differences (CBCT minus AVE) in regions-of-interest encompassing the tumour are within 44 HU for slow mode, increasing up to 75 HU for fast mode. Target volumes contoured using HU thresholding on slow mode CBCTs are smaller than those on the AVE/MIP by up to 7%/29%. HU thresholding was not applied to fast mode CBCTs because motion artefacts were judged to be too severe. Gamma pass rates for dose distributions calculated on fast or slow mode CBCTs compared to the AVE are <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo>≥</mml:mo> </mml:math> 99% (criteria: 1%, 1 mm, 10% dose threshold). Dose differences (fast mode CBCT minus AVE) are larger for larger amplitudes and longer periods, and tend toward negative values. Dose differences (slow mode CBCT minus AVE) are generally smaller and more consistent across all amplitudes and periods considered. Conclusions . Dosimetric perturbations resulting from motion artefacts are not severe for the amplitudes and periods considered. However, motion artefacts (especially in fast mode) have implications for image registration, target contouring, and treatment plan optimization for ART.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it