Localization of the Subthalamic Nucleus: Optimization with Susceptibility-Weighted Phase MR Imaging
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: On clinical MR images, the subthalamic nuclei (STN) are poorly delineated from adjacent structures, impeding safe direct targeting for placement of electrodes in the treatment of Parkinson disease. Susceptibility-weighted MR phase imaging offers improved contrast and spatial resolution at reduced imaging times relative to clinically used T2-weighted spin-echo imaging for STN visualization. Our purpose was to assess STN visibility by using phase imaging, comparing phase and magnitude images obtained concurrently by using susceptibility-weighted imaging (SWI). The goal was to identify an efficient scanning protocol for high-quality phase images of STN. MATERIALS AND METHODS: Seventy-eight SWI scans were acquired at 3T by using different TEs and acceleration factors. STN visibility and delimitation from adjacent structures were scored from 0 (not interpretable) to 5 (excellent). Regression analyses assessed the relationship of STN visibility to scanning parameters RESULTS: STN were identified at all studied TEs on phase images. Visibility and delimitation of STN were consistently superior on phase images compared with magnitude images. Good visualization (score of >or=4) of STN on phase imaging occurred at a mean TE of 20.0 ms and a sensitivity encoding (SENSE) of 1.40. Scores of STN visualization on phase images were dependent on SENSE (P < .002) and TE (P < .031). Good delimitation of the STN on phase imaging occurred at a mean TE of 21.6 ms and a SENSE of 1.36. CONCLUSIONS: Visualization and delimitation of STN was superior on phase images and was achieved at 3T in <2.5 minutes. A TE of 20 ms and an acceleration factor of
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".