Reference dosimetry in magnetic fields: formalism and ionization chamber correction factors
Bibliographic record
Abstract
PURPOSE: Magnetic resonance imaging-guided radiotherapy (MRIgRT) provides superior soft-tissue contrast and real-time imaging compared with standard image-guided RT, which uses x-ray based imaging. Several groups are developing integrated MRIgRT machines. Reference dosimetry with these new machines requires accounting for the effects of the magnetic field on the response of the ionization chambers used for dose calibration. Here, the authors propose a formalism for reference dosimetry with integrated MRIgRT devices. The authors also examined the suitability of the TPR10 (20) and %dd(10)x beam quality specifiers in the presence of magnetic fields and calculated detector correction factors to account for the effects of the magnetic field for a range of detectors. METHODS: The authors used full-head and point-source Monte Carlo models of an MR-linac along with detailed detector models of an Exradin A19, an NE2571, and several PTW Farmer chambers to calculate magnetic field correction factors for six commercial ionization chambers in three chamber configurations. Calculations of ionization chamber response (performed with geant4) were validated with specialized Fano cavity tests. %dd(10)x values, TPR10 (20) values, and Spencer-Attix water-to-air restricted stopping power ratios were also calculated. The results were further validated against measurements made with a preclinical functioning MR-linac. RESULTS: The TPR10 (20) was found to be insensitive to the presence of the magnetic field, whereas the relative change in %dd(10)x was 2.4% when a transverse 1.5 T field was applied. The parameters chosen for the ionization chamber calculations passed the Fano cavity test to within ∼0.1%. Magnetic field correction factors varied in magnitude with detector orientation with the smallest corrections found when the chamber was parallel to the magnetic field. CONCLUSIONS: Reference dosimetry can be performed with integrated MRIgRT devices by using magnetic field correction factors, but care must be taken with the choice of beam quality specifier and chamber orientation. The uncertainties achievable under this formalism should be similar to those of conventional formalisms, although this must be further quantified.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".