Novel frequency selective B<sub>1</sub> focusing passive lenz resonators for substantial MRI signal-to-noise ratio amplification
Bibliographic record
Abstract
Abstract Objective. The need for increased sensitivity in magnetic resonance imaging (MRI) is crucial for its advancement as an imaging modality. The development of passive Lenz Resonators (LRs) for effective RF magnetic field (B1) focusing will improve MRI sensitivity via local amplification of MRI signal, thereby leading to more efficient diagnosis and patient treatment. Approach. While there are methods for amplifying the signal from specific nuclei in MRI, such as hyperpolarization, a general solution will be more advantageous and would work in combination with these preexisting methods. While the Lenz Lens proposed such a general solution based on Lenz’s law and the reciprocity principle, it came at the cost of limited signal enhancement. In this work, the first-in-kind prototype LR was conceived and examined as a general frequency-selective passive flux-focusing element for significant MRI signal enhancement. A 3.0 T Philips Achieva MRI was used to compare the signal from a phantom in the presence of Lenz Lenses, LRs, and control trials with neither component. Main results. An MRI investigation demonstrated an experimental amplification of the signal-to-noise ratio up to 80% using an MRI insert of two coaxial LRs due to superior B1 focusing. The resonators displayed consistent amplification, nearly independent of their x-position within the MRI bore. Significance. This behavior demonstrates the feasibility of imaging large objects of varying shapes without penalties for signal amplification using LRs. The LRs versatility in geometrical design and consistent signal amplifying abilities between pulse sequences should allow for the development of LRs suitable for most commonly used MRI setups.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".