Extension of the Fermi–Eyges most-likely path in heterogeneous medium with prior knowledge information
Bibliographic record
Abstract
Abstract Particle imaging suffers from poor spatial resolution due to the multiple Coulomb scattering deflections undergone by the particles throughout their path. To account for these deflections, a most-likely path (MLP) formalism was developed based on a Bayesian adaption of the Fermi–Eyges theory. Previous work calculated the MLP formalism in a homogeneous water medium as an initial estimate. However, this potentially reduces the accuracy of the MLP estimate as well as the achievable resolution of the subsequent tomographic reconstruction. This work investigates the potential gain of introducing prior-knowledge on the medium composition and density to improve the MLP accuracy. To do so, a Monte Carlo (MC) Geant4 algorithm was used to simulate protons ( n = 10 6 ) crossing three different anthropomorphic phantoms representing the lung, abdomen, and head. The prior-knowledge information is gathered from (1) the MC simulation for ground-truth (MLP-GT), or from (2) a recent DECT material decomposition technique (MLP-DECT). The reconstructed path accuracy using prior-knowledge methods is compared with (3) the path reconstructed in homogeneous water (MLP-Water) and (4) a path reconstruction method where the proton path is projected onto a Hull at the boundary of the phantom with a subsequent MLP-Water calculation (MLP-Hull). For each path reconstruction method, the maximal root-mean-square error (RMS max ) is compared between the reconstructed and the MC path. In every phantom, the RMS max is decreased between the MLP-Water and the three other path algorithms that take into account heterogeneities ( − 33 % for the lung, − 38 % for the abdomen and − 81 % for the head), with no significant differences between each (MLP-DECT, MLP-GT and MLP-Hull). In conclusion, the introduction of prior-knowledge in the MLP formalism decreases the RMS uncertainty to the MC path, but no further than the use of a simpler Hull contour algorithm. The use of this Hull algorithm is suggested for future particle imaging applications.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".