Image registration of a moving target phantom with helical tomotherapy: effect of the CT acquisition technique and action level proposal
Bibliographic record
Abstract
This study aims to quantify the effects of target motion and resultant motion artifacts in planning and megavoltage CT (MVCT) studies on the automatic registration processes of helical tomotherapy. Clinical and experimental data were used to derive an action level for patient repositioning on helical tomotherapy. Planning CT studies of a respiratory motion phantom were acquired using conventional and four-dimensional CT (4D CT) techniques. MVCT studies were acquired on helical tomotherapy in the presence and absence of target motion and were registered with different planning CT studies. The residual errors of the registration process were calculated from the registration values to quantify the ability of the process to detect 5 or 10 mm translations of the phantom in two directions. Twenty-seven registration combinations of MVCT inter-slice spacing, technique and resolution were investigated. The residual errors were used as an estimate of the localization error of the registration process, and the accuracy of couch repositioning was determined from couch position measurements during 866 treatment fractions. These two parameters were used to calculate the action level for patient repositioning on helical tomotherapy. Automatic registration of an MVCT study with 0% breathing phase, average intensity and maximum intensity 4D CT projections did not differ from that of an MVCT study with a conventional planning CT. Motion artifacts in the MVCT or planning CT studies changed the accuracy of the automatic registration process by less than 2.0%. The action level for patient repositioning using MVCT studies of 6 mm inter-slice spacing was determined to be 0.7, 1.1 and 0.6 mm in the x-, y- and z-directions, respectively. These action levels have the greatest effect on treatments for disease sites in the brain.
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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 | 0.012 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".