Signal‐to‐noise optimization for sodium MRI of the human knee at 4.7 Tesla using steady state
Bibliographic record
Abstract
Sodium magnetic resonance imaging of knee cartilage is a possible diagnostic method for osteoarthritis, but low signal-to-noise ratio yields low spatial resolution images and long scan times. For a given scan time, a steady-state approach with reduced repetition time and increased averaging may improve signal-to-noise ratio and hence attainable resolution. However, repetition time reduction results in increased power deposition, which must be offset with increased radiofrequency pulse length and/or reduced flip angle to maintain an acceptable specific absorption rate. Simulations varying flip angle, repetition time, and radiofrequency pulse length were performed for constant power deposition corresponding to ∼6 W/kg over the human knee at 4.7 T. For 10% agar, simulation closely matched experiment. For healthy human knee cartilage, a 37% increase in signal-to-noise ratio was predicted for steady-state over "fully relaxed" parameters while a 29% ± 4% increase was determined experimentally (n=10). Partial volume of cartilage with synovial fluid, inaccurate relaxation parameters used in simulation, and/or quadrupolar splitting may be responsible for this disagreement. Excellent quality sodium images of the human knee were produced in 9 mins at 4.7 T using the signal-to-noise ratio enhancing steady-state technique.
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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 | 0.001 | 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".