Improving intracranial arteriosclerosic stenosis MRI using wireless resonator array inserts
Bibliographic record
Abstract
This study investigated the efficacy of using wireless resonator inserts in conjunction with head and neck coils to enhance carotid artery imaging. A group of patients diagnosed with carotid plaque underwent MRI scans using a Siemens head and neck coil and wireless resonator insert. The results showed significant improvements in image quality, resolution and atherosclerotic plaque detection capabilities compared to traditional wired connections. The wireless setup minimizes interference and artifacts during imaging, promoting smoother, more reliable scanning. These findings highlight the potential of wireless coil technology to advance MRI imaging and improve clinical diagnosis and treatment of carotid artery-related diseases. Further research and optimization of the imaging protocol is required to maximize the benefits of this innovative approach. • First clinical application of wireless MRI coil for carotid plaque imaging: Demonstrated the use of wireless resonator inserts in combination with head and neck coils to enhance carotid artery MRI imaging. • Significantly improved image quality: Achieved significant improvements in SNR, resolution, and plaque detection capabilities compared to traditional wired configurations. • Minimized interference and artifacts: Wireless setup minimized imaging artifacts and interference, promoting smoother and more reliable scanning. • High diagnostic accuracy: Enhanced image quality and resolution led to improved diagnostic accuracy in assessing carotid artery-related diseases.
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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 | 0.000 | 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".