Sodium <scp>MRI</scp> of the skin using a surface coil to investigate and reduce signal loss and bias
Bibliographic record
Abstract
Abstract Purpose The purpose was to improve sodium MRI of human skin using a surface coil and twisted projection imaging with smaller, reshaped voxels. Methods Calf skin sodium images were acquired in 14 healthy adults using twisted projection imaging with short TE ˜ 0.1 ms, first with a volume coil and voxels (1.5 × 1.5 × 15 = 34 mm3) reflecting the widely adopted skin imaging protocol (VolPencil). A 5‐cm‐diameter surface coil then facilitated 5× smaller (0.8 × 0.8 × 10 = 6.4 mm3) voxels with similar signal to noise ratio (SNR) in the same 12‐min scan time (SurfPencil). “Pencil‐shaped” voxels were then replaced with “pancake‐shaped” (0.4 × 4 × 4 = 6.4 mm3) voxels, matching the anatomy of pressed flat skin (SurfPancake). Surface coil B1 was investigated with the novel use of spin‐3/2 simulation. Protocol modifications were tested for signal increase (reduced loss) and correlation with (bias by) skin thickness. Results Higher resolution SurfPencil yielded 44% ± 16% greater skin sodium image intensity than VolPencil, whereas SurfPancake yielded an additional 20% ± 9% (p < 1e−8), reflecting reduced signal loss. Over the 1.0 to 1.8 mm skin thickness across participants, sodium intensity significantly increased 56% ± 19% and 44% ± 12% for VolPencil and SurfPencil, respectively (p < 0.003), but not for SurfPancake, reflecting reduced bias. Imaging yielded skin sodium concentration of 34 ± 5 mM for SurfPancake. This is greater than the ˜20 mM measures from the widely adopted protocol, but simulation (matching experimental trends) identified a remaining 64% signal loss; compensation yields 95 ± 15 mM. Conclusion Surface coil imaging and “pancake” voxel reshaping increased skin sodium intensity and reduced bias by skin thickness. Simulated loss compensation yields skin sodium concentration similar to that measured by atomic absorption spectroscopy.
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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) | 0.002 | 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".