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 <or=1.5 are recommended to visualize STN by using this method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".