In vivo magnetic resonance imaging of the human cervical spinal cord at 3 Tesla
Bibliographic record
Abstract
PURPOSE: To demonstrate the feasibility of obtaining high-quality magnetic resonance (MR) images of the human cervical spinal cord in vivo at a magnetic field strength of 3 T and to optimize the signal contrast between gray matter, white matter, and cerebrospinal fluid (CSF) on 2D gradient recalled echo (GRE) images of the cervical spinal cord. MATERIALS AND METHODS: Using a custom-built, anatomically molded radio frequency (RF) surface coil, the repetition time and flip angle of a 2D GRE sequence were systematically varied in five volunteers to assess tissue contrast in the cervical spinal cord. RESULTS: The 2D GRE parameters for an optimal balance between gray-white matter and CSF-white matter contrast at 3 T were determined to be a time-to-repetition (TR) of 2000 msec and a flip angle of 45 degrees, with the constant short time-to-echo (TE) of 12 msec used in this study. Excellent tissue contrast and visualization of the internal anatomy of the spinal cord was demonstrated reproducibly in eight subjects using these optimal parameters. CONCLUSION: This study demonstrates that imaging the cervical spinal cord and delineating internal spinal cord structures such as gray and white matter is feasible at 3 T.
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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 | 0.000 | 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 | 0.001 | 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".