Theoretical tumor edge detection technique using multiple Bragg peak decomposition in carbon ion therapy
Bibliographic record
Abstract
Abstract Purpose: The range precision in carbon ion therapy is extremely sensitive to tissue density variations. A high energy carbon beam, after crossing a high-gradient edge parallel to the beam direction, suffers from range mixing leading to the detection of multiple Bragg peaks (BPs) varying intensity and water equivalent thickness (WET). The purpose of this work was to introduce a model that determines the position of a high-gradient edge based on information acquired from carbon transmission imaging. Methods: A model was derived to determine the lateral distance between the irradiation beam propagation axis and the edge position. To validate it, carbon beams were simulated and propagated through two parametric phantoms: (1) a bone cube in a water tank and (2) a semi-cylindrical bone insert in a water tank. The method was tested in a lung tumor case where range mixing led to more than two BPs being detected, requiring an iterative BP decomposition to determine the fraction of carbon ions crossing the materials surrounding the edge of interest. Results: The theoretical model predicted the edge position relative to the beam position with an error ≤1 mm for all studied cases with a maximum dose delivered of 24 μ Gy. Conclusions: The method presented here is a proof of principle. It does not take into account clinical uncertainties. However, this approach provided promising results suggesting that future extension to assess the impact of clinical uncertainties should be performed.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".