Magnetic-field-induced dose effects in MR-guided radiotherapy systems: dependence on the magnetic field strength
Bibliographic record
Abstract
Several institutes are currently working on the development of a radiotherapy treatment system with online MR imaging (MRI) modality. The main difference between their designs is the magnetic field strength of the MRI system. While we have chosen a 1.5 Tesla (T) magnetic field strength, the Cross Cancer Institute in Edmonton will be using a 0.2 T MRI scanner and the company Viewray aims to use 0.3 T. The magnetic field strength will affect the severity of magnetic field dose effects, such as the electron return effect (ERE): considerable dose increase at tissue air boundaries due to returning electrons. This paper has investigated how the ERE dose increase depends on the magnetic field strength. Therefore, four situations where the ERE occurs have been simulated: ERE at the distal side of the beam, the lateral ERE, ERE in cylindrical air cavities and ERE in the lungs. The magnetic field comparison values were 0.2, 0.75, 1.5 and 3 T. Results show that, in general, magnetic field dose effects are reduced at lower magnetic field strengths. At the distal side, the ERE dose increase is largest for B = 0.75 T and depends on the irradiation field size for B = 0.2 T. The lateral ERE is strongest for B = 3 T but shows no effect for B = 0.2 T. Around cylindrical air cavities, dose inhomogeneities disappear if the radius of the cavity becomes small relative to the in-air radius of the secondary electron trajectories. At larger cavities (r > 1 cm), dose inhomogeneities exist for all magnetic field strengths. In water-lung-water phantoms, the ERE dose increase takes place at the water-lung transition and the dose decreases at the lung-water transition, but these effects are minimal for B = 0.2 T. These results will contribute to evaluating the trade-off between magnetic field dose effects and image quality of MR-guided radiotherapy systems.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".