Visualization of thalamic nuclei on high resolution, multi‐averaged T<sub>1</sub> and T<sub>2</sub> maps acquired at 1.5 T
Bibliographic record
Abstract
The ability to differentiate noninvasively between the primary nuclear divisions of the thalamus has immediate clinical applicability for surgical planning and guidance of functional stereotactic procedures. Comparison of prior qualitative magnetic resonance imaging (MRI) studies carried out at field strengths of 1.5 and 4 Tesla have revealed contrast within the thalamus that varies with field strength, suggesting possible differences in the inherent T1 and T2 relaxation times of the constituent nuclei. We investigate this hypothesis through acquisition of high-resolution, multi-averaged deep-brain T1 and T2 maps of a healthy volunteer. Fourteen nuclei were identified using their center-of-mass coordinates (in Talairach space) and average T1 and T2 values obtained from regions of interest placed within each. Results from this analysis revealed significant differences in T1 and T2 between the nuclei with a T1 range from 700 to 1,400 ms and a T2 range from 89 to 122 ms, allowing visual discrimination between the major nuclei groups. Furthermore, the high-resolution images showed distinct borders of T1 and T2 hypointensity surrounding each nucleus, revealing structure not reported previously. These results confirm our hypothesis and demonstrate the potential high-resolution quantitative imaging for nucleus visualization and surgical planning.
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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 | 0.001 | 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) | 0.002 | 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".