Calibration‐free parallel transmission of the cervical, thoracic, and lumbar spinal cord at <scp>7T</scp>
Bibliographic record
Abstract
Abstract Purpose To address the limitations of spinal cord imaging at ultra‐high field (UHF) due to time‐consuming parallel transmit (pTx) adjustments. This study introduces calibration‐free offline computed universal shim modes that can be applied seamlessly for different pTx RF coils and spinal cord target regions, substantially enhancing spinal cord imaging efficiency at UHF. Methods A library of channel‐wise relative maps for the cervical spinal cord (six datasets) and thoracic and lumbar spinal cord (nine datasets) was constructed to optimize transmit homogeneity and efficiency for these regions. A tailored B0 shim was optimized for the cervical spine to enhance spatial magnetic field homogeneity further. The performance of the universal shims was validated using absolute saturation based mapping and high‐resolution 2D and 3D multi‐echo gradient‐recalled echo (GRE) data to assess the image quality. Results The proposed universal shims demonstrated a 50% improvement in efficiency compared to the default (zero phase) shim mode. homogeneity was also improved by 20%. The optimized universal shims achieved performance comparable to subject‐specific pTx adjustments, while eliminating the need for lengthy pTx calibration times, saving about 10 min per experiment. Conclusion The development of universal shims represents a significant advance by eliminating time‐consuming subject‐specific pTx adjustments. This approach is expected to make UHF spinal cord imaging more accessible and user‐friendly, particularly for non‐pTx experts.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".