Developing a phenomenological model of the proton trajectory within a heterogeneous medium required for proton imaging
Bibliographic record
Abstract
To develop an accurate phenomenological model of the cubic spline path estimate of the proton path, accounting for the initial proton energy and water equivalent thickness (WET) traversed. Monte Carlo (MC) simulations were used to calculate the path of protons crossing various WET (10-30 cm) of different material (LN300, water and CB2-50% CaCO3) for a range of initial energies (180-330 MeV). For each MC trajectory, cubic spline trajectories (CST) were constructed based on the entrance and exit information of the protons and compared with the MC using the root mean square (RMS) metric. The CST path is dependent on the direction vector magnitudes (|P0,1|). First, |P0,1| is set to the proton path length (with factor Λ(Norm)(0,1) = 1.0). Then, two optimal factor Λ(0,1) are introduced in |P0,1|. The factors are varied to minimize the RMS difference with MC paths for every configuration. A set of Λ(opt)(0,1) factors, function of WET/water equivalent path length (WEPL), that minimizes the RMS are presented. MTF analysis is then performed on proton radiographs of a line-pair phantom reconstructed using the CST trajectories. Λ(opt)(0,1) was fitted to the WET/WEPL ratio using a quadratic function (Y = A + BX(2) where A = 1.01,0.99, B = 0.43,- 0.46 respectively for Λ(opt)(0), Λ(opt)(1)). The RMS deviation calculated along the path, between the CST and the MC, increases with the WET. The increase is larger when using Λ(Norm)(0,1) than Λ(opt)(0,1) (difference of 5.0% with WET/WEPL = 0.66). For 230/330 MeV protons, the MTF10% was found to increase by 40/16% respectively for a thin phantom (15 cm) when using the Λ(opt)(0,1) model compared to the Λ(Norm)(0,1) model. Calculation times for Λ(opt)(0,1) are scaled down compared to MLP and RMS deviation are similar within standard deviation.B ased on the results of this study, using CST with the Λ(opt)(0,1) factors reduces the RMS deviation and increases the spatial resolution when reconstructing proton trajectories.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".