A simulation study on proton computed tomography (CT) stopping power accuracy using dual energy CT scans as benchmark
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Bibliographic record
Abstract
BACKGROUND: Accurate stopping power estimation is crucial for treatment planning in proton therapy, and the uncertainties in stopping power are currently the largest contributor to the employed dose margins. Dual energy x-ray computed tomography (CT) (clinically available) and proton CT (in development) have both been proposed as methods for obtaining patient stopping power maps. The purpose of this work was to assess the accuracy of proton CT using dual energy CT scans of phantoms to establish reference accuracy levels. MATERIAL AND METHODS: A CT calibration phantom and an abdomen cross section phantom containing inserts were scanned with dual energy and single energy CT with a state-of-the-art dual energy CT scanner. Proton CT scans were simulated using Monte Carlo methods. The simulations followed the setup used in current prototype proton CT scanners and included realistic modeling of detectors and the corresponding noise characteristics. Stopping power maps were calculated for all three scans, and compared with the ground truth stopping power from the phantoms. RESULTS: Proton CT gave slightly better stopping power estimates than the dual energy CT method, with root mean square errors of 0.2% and 0.5% (for each phantom) compared to 0.5% and 0.9%. Single energy CT root mean square errors were 2.7% and 1.6%. Maximal errors for proton, dual energy and single energy CT were 0.51%, 1.7% and 7.4%, respectively. CONCLUSION: Better stopping power estimates could significantly reduce the range errors in proton therapy, but requires a large improvement in current methods which may be achievable with proton CT.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it