Dosimetric variation due to CT inter-slice spacing in four-dimensional carbon beam lung therapy
Bibliographic record
Abstract
When CT data with thick slice thickness are used in treatment planning, geometrical uncertainty may induce dosimetric errors. We evaluated carbon ion dose variations due to different CT slice thicknesses using a four-dimensional (4D) carbon ion beam dose calculation, and compared results between ungated and gated respiratory strategies. Seven lung patients were scanned in 4D mode with a 0.5 mm slice thickness using a 256-multi-slice CT scanner. CT images were averaged with various numbers of images to simulate reconstructed images with various slice thicknesses (0.5-5.0 mm). Two scenarios were studied (respiratory-ungated and -gated strategies). Range compensators were designed for each of the CT volumes with coarse inter-slice spacing to cover the internal target volume (ITV), as defined from 4DCT. Carbon ion dose distribution was computed for each resulting ITV on the 0.5 mm slice 4DCT data. The accumulated dose distribution was then calculated using deformable registration for 4D dose assessment. The magnitude of over- and under-dosage was found to be larger with the use of range compensators designed with a coarser inter-slice spacing than those obtained with a 0.5 mm slice thickness. Although no under-dosage was observed within the clinical target volume (CTV) region, D95 remained at over 97% of the prescribed dose for the ungated strategy and 95% for the gated strategy for all slice thicknesses. An inter-slice spacing of less than 3 mm may be able to minimize dose variation between the ungated and gated strategies. Although volumes with increased inter-slice spacing may reduce geometrical accuracy at a certain respiratory phase, this does not significantly affect delivery of the accumulated dose to the target during the treatment course.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".