Sci‐Fri PM: Radiation Therapy, Planning, Imaging, and Special Techniques ‐ 01: On the use of proton radiography to reduce beam range uncertainties and improve patient positioning accuracy in proton therapy
Bibliographic record
Abstract
To present two related developments of proton radiography (pRad) to minimize range uncertainty in proton therapy. The first combines a pRad with an X‐ray CT to produce a patient‐specific relative stopping power (RSP) map. The second aims to improve the pRad spatial resolution for accurate registration prior to the first. The enhanced‐pRad can also be used in a novel proton‐CT reconstruction algorithm. Monte Carlo pRad were computed from three phantoms; the Gammex, the Catphan and an anthropomorphic head. An optimized cubic‐spline estimator derives the most likely path. The length crossed by the protons voxel‐by‐voxel was calculated by combining their estimated paths with the CT. The difference between the theoretical (length×RSP) and measured energy loss was minimized through a least squares optimization (LSO) algorithm yielding the RSP map. To increase pRad spatial resolution for registration with the CT, the phantom was discretized into voxels columns. The average column RSP was optimized to maximize the proton energy loss likelihood (MLE). Simulations showed precise RSP (<0.75%) for Gammex materials except low‐density lung (<1.2%). For the head, accurate RSP were obtained (µ=−0.10%1.5σ=1.12%) and the range precision was improved (ΔR80 of −0.20±0.35%). Spatial resolution was increased in pRad (2.75 to 6.71 lp/cm) and pCT from MLE‐enhanced pRad (2.83 to 5.86 lp/cm). The LSO decreases the range uncertainty (R80σ<1.0%) while the MLE‐enhanced pRad spatial resolution (+244%) and is a great candidate for pCT reconstruction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".