Poster - Thur Eve - 13: Quantifying specific absorption rate of shielded RF coils through electromagnetic simulations for 7-T MRI
Bibliographic record
Abstract
Ultra-high field MRI has many advantages such as increasing spatial resolution and exploiting contrast never before seen in-vivo. This contrast has been shown to be beneficial for many applications such as monitoring early and late effect to radiation therapy and transient changes during disease to name a few. However, at higher field strengths the RF wave, needed to for transmitting and receiving signal, approaches that of the head. This leads to constructive and deconstructive interference and a non -uniform flip angle over the volume being imaged. A transmit or transceive RF surface coil arrays is currently a method of choice to overcome this problem; however, mutual inductance between elements poses a significant challenge for the designer. A method to decouple elements in such an array is by using circumferential shielding; however, the potential benefits and/or disadvantages have not been investigated. This abstract primarily focuses on understanding power deposition - measured through Specific Absorption Rate - in the sample using circumferentially shielded RF coils. Various geometries of circumferentially shielded coils are explored to determine the behaviour of shield width and its effect on required transmit power and power deposition to the sample. Our results indicate that there is an optimization on shield width depending on the imaging depth. Additionally, the circumferential shield focuses the field more than unshielded coils, meaning that slight SAR may even be lower for circumferential shielded RF coils in array.
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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 | 0.001 | 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".