Radiation induced currents in MRI RF coils: application to linac/MRI integration
Bibliographic record
Abstract
The integration of medical linear accelerators (linac) with magnetic resonance imaging (MRI) systems is advancing the current state of image-guided radiotherapy. The MRI in these integrated units will provide real-time, accurate tumor locations for radiotherapy treatment, thus decreasing geometric margins around tumors and reducing normal tissue damage. In the real-time operation of these integrated systems, the radiofrequency (RF) coils of MRI will be irradiated with radiation pulses from the linac. The effect of pulsed radiation on MRI radio frequency (RF) coils is not known and must be studied. The instantaneous radiation induced current (RIC) in two different MRI RF coils were measured and presented. The frequency spectra of the induced currents were calculated. Some basic characterization of the RIC was also done: isolation of the RF coil component responsible for RIC, dependence of RIC on dose rate, and effect of wax buildup placed on coil on RIC. Both the time and frequency characteristics of the RIC were seen to vary with the MRI RF coil used. The copper windings of the RF coils were isolated as the main source of RIC. A linear dependence on dose rate was seen. The RIC was decreased with wax buildup, suggesting an electronic disequilibrium as the cause of RIC. This study shows a measurable RIC present in MRI RF coils. This unwanted current could be possibly detrimental to the signal to noise ratio in MRI and produce image artifacts.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".