SU‐E‐E‐15: Design of a Water Calorimeter for Dual Use in An Integrated MRI‐Linac and Gamma‐Knife
Bibliographic record
Abstract
Purpose: To design a water calorimeter for dual use in an integrated MRI‐linac and Gamma‐Knife. In calorimetry, dose to water is measured based on the assumption that energy absorbed in a sensitive volume is completely converted to temperature rise ₍ΔΤ₎ according to the specific heat capacity of the medium c: D=c*ΔT*k, where k is heat transfer correction factor and compensates for heat gain or loss at point of measurement due to conductive effects. Methods: A commercial finite element method software package was used to model four different water calorimeter designs. The long term (48 h) thermal stability of each design was accurately modeled, and the optimization of the final design was based on evaluation of the standard deviation of k for ten consecutive irradiation runs (lower standard deviation translates to greater thermal stability). Several insulator materials of varying thicknesses were investigated, and a sensitivity study of thermal stability to variations in ambient temperature fluctuations was undertaken. Specifically, we evaluated the effect of possible variations in coolant temperature circulating around the calorimeter tank in several scenarios (constant, slowly increasing, or fluctuating). Results: Due to MRI‐compatibility requirements, the calorimeter is to be built entirely out of plastic. Among all insulation materials tested, solid state aerogel‐based insulation resulted in least heat loss and thermal stability. The final design is cylindrical on top (to be used upright in MRI‐linac) and semi‐spherical at bottom (for use in GammaKnife). The range of k was found to be 1.002 ± 0.013 (k = 1), comparable with a k of 1.002 ± 0.014 for typical water calorimeters used in high energy beams. A long term stability of 0.36 µK/hr was evaluated. Conclusion: Optimization of a water calorimeter design for dual use in MRI‐linac and Gamma‐Knife has been completed and the device is currently in production. Supported by NSERC RGPIN 435608
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".