Water calorimetry in MR‐linac: Direct measurement of absorbed dose and determination of chamber
Bibliographic record
Abstract
Purpose To use a portable 4°C cooled MR‐compatible water calorimeter to measure absorbed dose in a magnetic resonance‐guided radiation therapy (MRgRT) system. Furthermore, to use the calorimetric dose results and direct cross‐calibration to experimentally measure the combined beam quality and magnetic field correction factor ( ) of a clinically used reference‐class ionization chamber placed under the same radiation field. Methods An Elekta Unity MR‐linac (7 MV FFF, B = 1.5 T) was used in this study. Measurements were taken using the in‐house designed and built water calorimeter. Following preparation and cooling of the system, the MR‐compatible calorimeter was positioned using a combination of MR and EPID imaging and the dose to water was measured by monitoring the radiation‐induced temperature change. Immediately after the calorimetric measurements, an A1SL ionization chamber was placed inside the calorimeter for direct cross‐calibration. The results allowed for a direct and absolute experimental measurement of for this chamber and comparison against existing Monte Carlo values. Results The calorimeter was successfully positioned using imaging in under an hour. The 1‐hour setup time is from the time the calorimeter leaves storage to the first calorimetric measurement. Absorbed dose was successfully measured with a relative combined standard uncertainty of 0.71 % ( k = 1). Through a cross‐calibration, the for an Exradin A1SL ionization chamber, set up perpendicular to the incident photon beam and opposite to the direction of the Lorentz force, was directly determined in water in absolute terms to be 0.977 ± 0.010. The currently published results, obtained via Monte Carlo calculations, agree with experimental measurements in this work within combined uncertainties. Conclusions A novel design of an MR‐compatible water calorimeter was successfully used to measure absorbed dose in an MR‐linac and determine an experimental value of for a clinically used ionization chamber.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".