Frequency Selective Flux Focusing Passive Lenz Resonators for Substantial MRI Signal-to-Noise Ratio Amplification
Bibliographic record
Abstract
The need for increased sensitivity in magnetic resonance imaging (MRI) is crucial for its advancement as an imaging modality. The amplification of MRI signal will lead to more efficient diagnosis and patient treatment. While there are methods that exist to amplify the signal from specific nuclei in MRI, such as hyperpolarization, a general solution will be more advantageous and would work in conjunction with these preexisting methods. While the Lenz Lens proposed such a general solution based on the reciprocity principle, it came at the cost of minimal signal enhancement. In this work, the first-in-kind prototype Lenz Resonator was conceived and examined as a general frequency selective passive flux focusing element for significant MRI signal enhancement. A 3T Philips Achieva MRI was used to compare signal from samples in the presence of Lenz Lenses, Lenz Resonators, and control trials with neither component. An MRI investigation demonstrated an experimental amplification of the signal-to-noise ratio up to $80 \%$ using an MRI insert of two coaxial square Lenz Resonators. Resonators displayed consistent amplification, nearly independent of their position on the axis of the target within the MRI bore. This behavior demonstrates the feasibility of imaging large objects of varying shapes without penalties for signal amplification.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".