Feasibility of energy adaptive angular meshing for perpendicular and parallel magnetic fields in a grid based Boltzmann solver
Bibliographic record
Abstract
PURPOSE: To develop the enabling algorithmic techniques which allow forward-peaked adaptive angular meshing to be compatible with angular advection of magnetic fields within a deterministic Grid Based Boltzmann Solver (GBBS) for MRI-guided radiotherapy, and establish appropriate energy adaptive meshing schemes which minimize total numerical degrees of freedom while preserving high dosimetric accuracy for parallel and perpendicular magnetic fields. METHODS: A framework to independently adapt angular mesh resolution and basis function refinement of forward and backscattering hemispheres is developed, uniquely accommodating angular advection introduced by magnetic fields. Upwind stabilization techniques to accurately transfer fluence between hemispheres having different discretization are established. To facilitate oblique beam and magnetic field orientations, cardinal forward-peaked mesh orientations were devised to balance requirements for acyclic space-angle sweep ordering, while ensuring the beam predominantly overlaps the forward hemisphere. Energy-dependent fluence anisotropy is investigated, leading to adaptive angular meshing schemes for parallel and perpendicular magnetic fields. Calculated dose distributions were validated against GEANT4 Monte Carlo calculations on slab geometry and anthropomorphic phantoms. RESULTS: Forward-peaked and isotropic energy adaptive angular meshing schemes were developed for parallel and perpendicular magnetic fields respectively, which reduce the number of elements solved by 52.8% and 47.7% respectively compared to static discretization using 32 quadratic elements while retaining over 97% of points passing the gamma 1%/1 mm criterion against Monte Carlo. CONCLUSIONS: Techniques to preserve angular upwind-stabilization between hemispheres of a forward-peaked mesh and establish an acyclic directed space-angle sweep graph enabled energy-adaptive meshing schemes to be developed while accurately solving for magnetic fields. This substantially reduced the numerical degrees of freedom while retaining excellent dosimetric agreement with Monte Carlo. These algorithmic underpinnings contribute towards a fast deterministic GBBS for MRI-guided radiotherapy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".