Monte Carlo investigation of sub-millimeter range verification in carbon ion radiation therapy using interaction vertex imaging
Bibliographic record
Abstract
Abstract In hadrontherapy, particularly carbon ion radiation therapy, a characteristic dose distribution precisely delivers maximum dose at the beam endpoint, or Bragg peak, with relatively low dose to the surrounding tissue. As the position of the Bragg peak is highly dependent on patient anatomy and physiology, precise range verification techniques are needed to ensure that the prescribed dose is properly targeted to tumours while sparing healthy tissue. We simulated treatments of a homogeneous phantom using Geant4, and applied a novel Interaction Vertex Imaging (IVI) reconstruction, combining single-particle reconstruction with a coincidence technique, and using a software filter to reduce uncertainty introduced by straggling and multiple scattering in the target. Interaction vertices generated by the most precise Triangulation IVI method were localized to an average of 3.5 mm from the true position of the reaction, simulating a realistic charged particle detection system. No event-by-event information from a beam tracking detector was used in reconstruction. Filtered longitudinal vertex distributions were fit to logistic functions, characterizing the distal edge closest to the Bragg peak. Comparing the position of this distal edge between simulations allowed us to accurately determine if two treatments correctly targeted the same depth. After performing a linear calibration, the depth difference between two treatments could be determined with sub-millimeter precision under clinical conditions (10 6 –10 7 incident 12 C ions), allowing range verification to be performed for each depth setting in a pencil beam scanned treatment plan.
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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 | 0.003 |
| 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 | 0.001 | 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".