Optimization of megavoltage CT scan registration settings for brain cancer treatments on tomotherapy
Bibliographic record
Abstract
This study aims to determine the settings that provide the optimal clinical accuracy and consistency for the registration of megavoltage CT (MVCT) with planning kilovoltage CT image sets on the Hi-ART tomotherapy system. The systematic offset between the MVCT and the planning kVCT was determined by registration of multiple MVCT scans of a head phantom aligned with the planning isocentre. Residual error vector lengths and components were used to quantify the alignment quality for the phantom shifted by 5 mm in different directions obtained by all 27 possible combinations of MVCT inter-slice spacing, registration techniques and resolution. MVCT scans with normal slices are superior to coarse slices for registration of shifts in the superior-inferior, lateral and anterior-posterior directions. Decreasing the scan length has no detrimental effect on registration accuracy as long as the scan lengths are larger than 24 mm. In the case of bone technique and fine resolution, normal and fine MVCT scan slice spacing options give similar accuracy, so normal mode is preferable due to shorter procedure and less delivered dose required for patient set-up. A superior-inferior field length of 24-30 mm, normal slice spacing, bone technique, and fine resolution is the optimum set of registration settings for MVCT scans of a Rando head phantom acquired with the Hi-ART tomotherapy system, provided the registration shifts are less than 5 mm.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".