Fast diffusion tensor imaging and tractography of the whole cervical spinal cord using point spread function corrected echo planar imaging
Bibliographic record
Abstract
Diffusion tensor imaging has been used in a number of spinal cord studies, but severe distortions caused by susceptibility induced field inhomogeneities limit its applicability to investigate small volumes within acceptable acquisition times. A way to evaluate image distortions is to map the point spread function of the voxel intensity in a reference scan. In this study, the point spread function was mapped for an echo-planar imaging sequence in the human cervical spinal cord with isotropic resolution and large field of view. Correction with the point spread function map improved anatomical consistency, and full cervical tractography was thereby possible from a C1 seed region in healthy controls and one individual with spinal cord injury. It is suggested that point spread function mapping of the spinal cord can be used in combination with sequence-based methods for reduction of susceptibility artifacts or in high-field imaging settings where off-resonance effects are pronounced.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".