Morphometric and signal intensity benchmarks of 3D CRANI MR neurography sequence for extraforaminal cranial and occipital nerves visualization: a pilot study
Bibliographic record
Abstract
PURPOSE: Lack of evidence exists related to the reporting of benchmark values of MR neurography sequences required for extraforaminal cranial and occipital nerve visualization. This study aimed to establish benchmarks of morphometric and signal intensity values of 3D CRANI MR neurography sequence in healthy subjects. METHODS: A total of 10 healthy participants (5 males, 5 females; age range:14-83 years) were recruited. Imaging was conducted using a 3.0 Tesla MRI system fitted with a 32-channel head coil. The assessed extraforaminal cranial and occipital nerves included auriculotemporal, buccal, facial, greater occipital, hypoglossal, inferior alveolar, lingual, mandibular, masseteric, and maxillary. These nerves were semi-automatically segmented and divided into five segments: proximal, mid-proximal, middle, mid-distal, and distal. Measurements were performed for per nerve and segment diameter, signal intensity, apparent signal-to-noise (aSNR) and apparent nerve-muscle contrast-to-noise ratios (aNMCNR). RESULTS: All nerves exhibited a decreasing trend in diameter and signal intensity from the proximal to the distal end, except for the facial, maxillary, and auriculotemporal nerves. The mid-proximal section of the nerves under examination showed notably higher values for diameter (p < 0.01), signal intensity (p < 0.0001), and aNMCNR (p < 0.05). On the other hand, the distal segment recorded the lowest values across all parameters. The aSNR and aNMCNR values confirmed good discrimination of each observed nerve. CONCLUSIONS: The proposed benchmark for 3D CRANI MR neurography enhances the neuroradiological understanding of cranial and occipital nerves. It could act as a reference guide in various head and neck scenarios, particularly when distinguishing between healthy and pathological conditions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".