SU‐E‐J‐239: Influence of RF Coil Materials On Surface and Buildup Dose From a 6MV Photon Beam
Bibliographic record
Abstract
Purpose: In order to perform real time tumour tracking using an integrated Linac‐MR, images have to be acquired during irradiation. MRI uses RF coils in close proximity to the imaged volume. Given current RF coil designs this means that the high energy photons will be passing through the coil before reaching the patient. This study experimentally investigates the dose modifications that occur due to the presence of various RF coil materials in the treatment beam. Methods: Polycarbonate, copper or aluminum tape, and Teflon were used to emulate the base, conductor and cover respectively of a surface RF coil. These materials were placed at various distances from the surface of polystyrene or solid water phantoms which were irradiated in the presence of no magnetic field, a transverse 0.2T magnetic field, and a parallel 0.2T magnetic field. Percent depth doses were measured using ion chambers. Results: A significant increase in surface and buildup dose is observed. The surface dose is seen to decrease with an increasing separation between the emulated coil and the phantom surface, when no magnetic field is present. When a transverse magnetic field is applied the surface dose decreases faster with increasing separation, as some of the electrons created in the coil are curved away from the phantom's surface. When a parallel field is present the surface dose stays approximately constant for small separations, only slightly decreasing for separations greater than 5cm, since the magnetic field focuses the electrons produced in the coil materials not allowing them to scatter. Conclusion: Irradiating a patient through an RF coil leads to an increase in the surface and buildup doses. Mitigating this increase is important for the successful clinical use of either a transverse or a parallel configuration Linac‐MR unit. This project is partially supported by an operating grant from the Canadian Institute of Health Research (CIHR MOP 93752)
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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 | 0.000 |
| 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) | 0.001 | 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".