A smart switching system to enable automatic tuning and detuning of metamaterial resonators in MRI scans
Bibliographic record
Abstract
We present a radio-frequency-activated switching system that can automatically detune a metamaterial resonator to enhance magnetic resonance imaging (MRI) performance. Local sensitivity-enhancing metamaterials typically consist of resonant components, which means that the transmitted radio frequency field is spatially inhomogeneous. The switching system shows for the first time that a metamaterial resonator can be detuned during transmission and tuned during reception using a digital circuit. This allows a resonating system to maintain homogeneous transmit field while maintaining an increased receive sensitivity. As a result, sensitivity can be enhanced without changing the system-provided specific absorption rate (SAR) models. The developed digital circuit consists of inductors sensitive to the transmit radio-frequency pulses, along with diodes acting as switches to control the resonance frequency of the resonator. We first test the automatic resonator detuning on-the-bench, and subsequently evaluate it in a 1.5 T MRI scanner using tissue-mimicking phantoms. The scan results demonstrate that the switching mechanism automatically detunes the resonator in transmit mode, while retaining its sensitivity-enhancing properties (tuned to the Larmor frequency) in receive mode. Since it does not require any connection to the MRI console, the switching system can have broad applications and could be adapted for use with other types of MRI scanners and field-enhancing resonators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".