Patient dosimetry for hybrid MRI‐radiotherapy systems
Bibliographic record
Abstract
A novel geometry has been proposed for a hybrid magnetic resonance imaging (MRI)-linac system in which a 6 MV linac is mounted on the open end of a biplanar, low field (0.2 T) MRI magnet on a single gantry that is free to rotate around the patient. This geometry creates a scenario in which the magnetic field vector remains fixed with respect to the incident photon beam, but moves with respect to the patient as the gantry rotates. Other proposed geometries are characterized by a radiation source rotating about a fixed cylindrical magnet where the magnetic field vector remains fixed with respect to the patient. In this investigation we simulate the inherent dose distribution patterns within the two MRI-radiation source geometries using PENELOPE and EGSnrc Monte Carlo radiation transport codes with algorithms implemented to account for the magnetic field deflection of charged particles. Simulations are performed in phantoms and for clinically realistic situations. The novel geometry results in a net Lorentz force that remains fixed with respect to the patient (in the cranial-caudal direction) and results in a cumulative influence on dose distribution for a multiple beam treatment scenario. For a case where patient anatomy is reasonably homogeneous (brain plan), differences in dose compared to a conventional (no magnetic field) case are minimal for the novel geometry. In the case of a lung plan where the inhomogeneous patient anatomy allows for the magnetic field to have significant influence on charged particle transport, larger differences occur in a predictable manner. For a system using a fixed cylindrical geometry and higher magnetic field (1.5 T), differences from the case without a magnetic field are significantly greater.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".