SU‐EE‐A3‐03: Radiation Induced Effects in An 8.5 MHz Magnetic Resonance Imaging Coil
Bibliographic record
Abstract
Purpose: Few research groups are involved in integrating magnetic resonance imaging (MRI) with a linear accelerator in order to obtain real‐time MRI images during the treatment beam‐on. Therefore, the radio frequency (RF) coils used in MRI will be irradiated during this real‐time image guided radiotherapy. The radiation effects on RF coils include instantaneous induced currents and long term radiation damage to components. These effects are potential obstacles facing linac‐MRI integration. This work measures and characterizes the instantaneous effect of pulsed radiation on MRI coils. Method and Materials: A CAT Solenoid coil was placed inside an RF cage, to remove RF noise, and connected to a current amplifier. The amplifier output was sent through RF filters to the RF cage exterior and then to an oscilloscope. The cage was placed in the pulsed beam of a linac and the current induced by pulsed radiation was measured by the oscilloscope; the waveforms were then transferred to a PC and power spectral density was calculated. Results: The RF cage was very effective in eliminating the extraneous RF noise from the radiation induced signal. The induced signal is only present when the radiation beam is on and incident upon the coil. The power spectrum of the induced current indicates that most of the power is contained below 1 MHz, but there is signal is present at the coil resonance frequency (8.5 MHz). The induced current a) occurs mainly in the copper winding, b) is reduced if buildup material is placed on the coil and c) increases linearly with the dose rate. Conclusion: Radiation induced current is present in MRI coils, but its impact on imaging is yet to be determined. This, along with magnetic field impact on induced current will be examined using the functioning 0.2 Tesla linac‐MR prototype.
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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".