Detunable wireless ladder resonator inserts for enhanced SNR of local array coil at 1.5T MRI
Bibliographic record
Abstract
Abstract Background Magnetic resonance imaging (MRI) is a non‐invasive technique that produces high‐resolution images with excellent soft‐tissue contrast, crucial for diagnosing various medical conditions. A key factor in MRI quality is the signal‐to‐noise ratio (SNR), which directly affects image clarity. To enhance SNR, passive inserts like high‐permittivity dielectric pads or metamaterials are used between the tissue and coil. However, this method faces challenges such as detuning during radiofrequency (RF) transmission, which can interfere with the transmit field ( B 1 + ) and pose safety issues. Purpose This study proposes a novel method to enhance SNR in MRI by using a non‐closed lumped‐element ladder resonator as a wireless insert. The aim is to optimize this ladder resonator through simulations and validate its performance in both phantom and in vivo MRI experiments. Methods The ladder resonator was designed and optimized through electromagnetic (EM) and RF circuit simulations using Ansys HFSS software. Various configurations (4, 6, 8, and 10 rungs) were tested. The optimized 8‐rung resonator was then fabricated and evaluated against a single‐loop resonator of the same size. MRI experiments were conducted using a 1.5T Siemens MRI scanner, assessing SNR improvements in both phantoms and volunteers. Results Simulation results indicated that the ladder resonator significantly improved local SNR compared to the single‐loop resonator. The 8‐rung ladder resonator provided the best performance. Experimental results corroborated these findings, with the 8‐rung ladder resonator showing SNR improvements up to 4.7 times in human head images, compared to the standard head array alone. Conclusions The study demonstrates that wireless ladder resonators can significantly enhance SNR in MRI, offering superior performance and more uniform sensitivity compared to traditional single‐loop designs. The detunable feature of the ladder resonator ensures it does not interfere with the RF field, making it suitable for routine clinical use. The simplicity, cost‐effectiveness, and compatibility with various MRI platforms underscore its potential for widespread clinical adoption.
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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 | 0.001 |
| Open science | 0.000 | 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".