SU‐E‐J‐84: Radiation Induced Currents in RF Coil Conductors: Measurements and Simulations
Bibliographic record
Abstract
Purpose: The integration of a medical linac with magnetic resonance imaging (MRI) has the potential to provide exquisite soft tissue contrast and real time imaging during the radiation treatment. However, during a real time treatment‐imaging session, the direct irradiation of MRIˈs radiofrequency (RF) coil by the pulsed x‐ray beam induces a current in the conductor of the RF coil due to the release of Compton electrons. This radiation induced current (RIC) can potentially degrade the SNR in acquired MRI images. The present work investigates methods of minimizing this RIC both experimentally and through Monte Carlo simulations.Methods: Copper and aluminum metal plates emulating the conductors used in RF coils were connected to an amplifier and placed in an RF cage. The plates (i.e. “detector”) were irradiated by the linacˈs pulsed 6 MV beam through the RF cage. The induced signal was measured by an oscilloscope and recorded using a PC. Various materials were used as buildup in an attempt to establish electronic equilibrium in the “detectors” ‐ thus removing the undesired RIC. A Monte Carlo script was written which counts the amount of charge entering and leaving a specified “detector volume”, and determines the net change in charge per primary history as a measure of RIC. The simulation geometry mimics the experimental setup.Results: It has been clearly demonstrated by both measurements and simulations that buildup of the same material as the conductor will reestablish electronic equilibrium and remove the RIC. Also, using a polymer with a density close to that of the conductor (i.e. Teflon with aluminum) for buildup will reduce the RIC to negligible amplitude Conclusions: With the proper combination of coil conductor and buildup, the RIC can be reduced to negligible amplitudes. Future work will assess the importance of RIC for the SNR in MRI images.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".