<scp>RF</scp> shimming in the cervical spinal cord at <scp>7 T</scp>
Bibliographic record
Abstract
Abstract Purpose Advancing the development of 7 T MRI for spinal cord imaging is crucial for the enhanced diagnosis and monitoring of various neurodegenerative diseases and traumas. However, a significant challenge at this field strength is the transmit field inhomogeneity. Such inhomogeneity is particularly problematic for imaging the small, deep anatomical structures of the cervical spinal cord, as it can cause uneven signal intensity and elevate the local specific absorption ratio, compromising image quality. This multisite study explores several RF shimming techniques in the cervical spinal cord. Methods Data were collected from 5 participants between two 7 T sites with a custom 8Tx/20Rx parallel transmission coil. We explored two radiofrequency (RF) shimming approaches from an MRI vendor and four from an open‐source toolbox, showcasing their ability to enhance transmit field and signal homogeneity along the cervical spinal cord. Results The circularly polarized (CP), coefficient of variation (CoV), and specific absorption rate (SAR) efficiency shim modes showed the highest B1+ efficiency, and the vendor‐based “patient” and “volume” modes showed the lowest B1+ efficiency. The coefficient of variation method produced the highest CSF/spinal cord contrast on T2*‐weighted scans (ratio of 1.27 ± 0.03), and the lowest variation of that contrast along the superior–inferior axis. Conclusion The study's findings highlight the potential of RF shimming to advance 7 T MRI's clinical utility for central nervous system imaging by enabling more homogenous and efficient spinal cord imaging. Additionally, the research incorporates a reproducible Jupyter Notebook, enhancing the study's transparency and facilitating peer verification.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".