SU‐E‐J‐37: Combining Proton Radiography and X‐Ray CT Information to Better Estimate Relative Proton Stopping Power in a Clinical Environment
Bibliographic record
Abstract
Purpose: In standard proton therapy clinical practice, proton stopping power uncertainties are in the order of 3.5%, which affects the ability of placing the proton Bragg peak at the edge of the tumor. The innovating idea of this project is to approach the uncertainty problem in RSP by using combined information from X‐ray CT and proton radiography along a few beam angles. In addition, this project aims to quantify the systematic error introduced by the theoretical models (Janni, ICRU49, Bischel) for proton stopping power in media. Methods: A 3D phantom of 36 cm3 composed of 9 materials randomly placed is created. Measured RSP values are obtained using a Gammex phantom with a proton beam. Theoretical RSP values are calculated with Beth‐Block equation in combination with three databases (Janni, ICRU49 and Bischel). Clinical RSP errors are simulated by introducing a systematic (1.5%, 2.5%, 3.5%) and a random error (+/−0.5%) to the theoretical RSP. A ray‐tracing algorithm uses each of these RSP tables to calculate energy loss for proton crossing the phantom through various directions. For each direction, gradient descent (GD) method is done on the clinical RSP table to minimize the residual energy difference between the simulation with clinical RSP and with theoretical RSP. The possibility of a systematic material dependent error is investigated by comparing measured RSP to theoretical RSP as calculated from the three models. Results: Using 10,000 iterations on GD algorithm, RSP differences between theoretical values and clinical RSP have converged (<1%) for each error introduced. Results produced with ICRU49 have the smallest average difference (0.021%) to the measured RSP. Janni (1.168%) and Bischel (−0.372%) database shows larger systematic errors. Conclusion: Based on these results, ray‐tracing optimisation using information from proton radiography and X‐ray CT demonstrates a potential to improve the proton range accuracy in a clinical environment.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".