Radiofrequency excitation–related <sup>23</sup>Na MRI signal loss in skeletal muscle, cartilage, and skin
Bibliographic record
Abstract
Purpose To assess the sodium MRI signal loss resulting from typically used RF excitation pulses in human skeletal muscle, patellar cartilage, and skin. Methods A double flip‐angle experiment was performed 3 times on the knees of 5 healthy volunteers with prescribed ω1 = γB1 of 1.67 kHz, 0.333 kHz, and 0.167 kHz. This was done to search for ω1‐dependent increased rates of sodium‐23 central resonance flipping known to result from residual quadrupole splitting (ωQ), as this flip‐angle effect is associated with signal loss. This study facilitated in vivo regression of Gaussian‐distributed residual quadrupole splitting SD (ωQ(SD)) as well as T2fast and T2slow. Signal loss predicted from simulation was then compared with images acquired using 90° RF pulse lengths of 0.5 ms, 0.25 ms, and 0.15 ms. Results Sodium‐23 central resonance flipping was significantly greater than prescribed (44% cartilage, 23% skin, 9% muscle) using ω1 = 0.167 kHz, but only 4% cartilage, 5% skin, and 2% muscle using ω1 = 1.67 kHz. Regression yielded ωQ(SD) = 420 ± 50 Hz for cartilage but no significant ωQ(SD) for skin or muscle. This points to rapid biexponential relaxation as the cause of the flip‐angle effect for skin/muscle. The T2fast(60%)/T2slow(40%) values were 1.6 ± 0.8 ms/16.1 ± 2.5 ms for muscle, 2.7 ± 0.9 ms/18.4 ± 2.5 ms for cartilage, and 0.4 ± 0.1 ms/9.3 ± 1.7 ms for skin. Simulation predicted signal loss of 6% ± 3%, 3% ± 1%, and 2% ± 1% for muscle, 16% ± 3%, 6% ± 1%, and 3% ± 1% for cartilage, and 26% ± 7%, 15% ± 4%, and 10% ± 3% for skin when using 90° RF pulse lengths of 0.5 ms, 0.25 ms, and 0.15 ms, matching experiment. Conclusion High‐power (short) RF pulses are necessary to reduce excitation‐related signal loss, particularly for sodium‐23 imaging of cartilage and skin.
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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 | 0.001 |
| 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".