High spatial resolution nerve‐specific DTI protocol outperforms whole‐brain DTI protocol for imaging the trigeminal nerve in healthy individuals
Bibliographic record
Abstract
Diffusion tensor imaging (DTI) can provide markers of axonal micro‐structure of the trigeminal nerve (cranial nerve five [CNV]), which may be affected in trigeminal neuralgia (TN) and other disorders. Previous attempts to image CNV have used low spatial resolution DTI protocols designed for whole‐brain acquisition that are susceptible to errors from partial volume effects, particularly with adjacent cerebrospinal fluid (CSF). The purpose of this study was to develop a nerve‐specific DTI protocol in healthy subjects that provides more accurate CNV tractography and diffusion quantification than whole‐brain protocols. Four DTI protocols were compared in five healthy individuals (age 22–45 years, three males) on a 3 T Siemens Prisma MRI scanner: two newly developed nerve‐specific high resolution (1.2 x 1.2 x 1.2 = 1.7 mm3) DTI protocols without (3.5 minutes) and with CSF suppression (fluid‐attenuated inversion recovery [FLAIR]; 7.5 minutes) with limited slice‐coverage, and two typical whole‐brain protocols with either isotropic (2 x 2 x 2 = 8 mm3) or thicker slice anisotropic (1.9 x 1.9 x 3 = 10.8 mm3) voxels. Deterministic tractography was used to identify the CNV and quantify bilateral fractional anisotropy (FA), and mean (MD), axial (AD) and radial diffusivity (RD). CNV volume was determined by manual tracing on T1‐weighted images. High spatial resolution nerve‐specific protocols yielded better delineation of CNV, with less distortions and blurring, and markedly different diffusion parameters (42% higher FA, 35% lower MD, 27% lower RD and 43% lower AD) compared with the two lower resolution whole‐brain protocols. The anisotropic whole‐brain protocol showed a positive correlation between CNV FA and volume. The high resolution nerve‐specific protocol with FLAIR yielded additional reductions in CNV AD and MD with a value of 1.0 x 10−3 mm2/s, approaching that expected for healthy young adult white matter. In conclusion, high resolution nerve‐specific DTI with FLAIR enhances the identification of CNV and provides more accurate quantification of diffusion compared with lower resolution whole‐brain approaches.
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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 | 0.001 | 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".