Kilovision: Thermal modeling of a kilovoltage x‐ray source integrated into a medical linear accelerator
Bibliographic record
Abstract
The thermal and thermo-mechanical (fatigue) properties of a stationary-anode kilovoltage x-ray source that can be integrated into the head of a medical linear accelerator have been modeled. A finite element program has been used to model two new target designs. The first design makes minor modifications to the existing target assembly of a Varian medical linear accelerator, while the second design adds an additional cooling tube, changes the target angle, and uses a tungsten-rhenium alloy rather than tungsten as the kilovoltage target material. The thermal calculations have been used to generate cyclic stress/strain values from which estimates of fatigue in the target designs have been made. Both kilovoltage and megavoltage operation have been studied. Analysis of the megavoltage operation shows that there are only small differences in the thermal and fatigue characteristics after the target assembly is modified to include a kilovoltage target. Thus, megavoltage operation should not be compromised. The first kilovoltage target design can handle a 900 W heat load (e.g., 120 kVp, 7.5 mA, 2 x 2 mm2 source size); the heat load being limited by the temperature at the surface of the cooling tubes and mechanical fatigue at the surface of the target. The second design can handle a 1250 W heat load (e.g., 120 kVp, approximately 10.4 mA, 2 x 2 mm2 source size). Our calculations show that installation of a kilovoltage x-ray target is practical from thermal and thermo-mechanical perspectives.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".