Detecting lesions in multiple sclerosis at 4.7 tesla using phase susceptibility‐weighting and T2‐weighting
Bibliographic record
Abstract
PURPOSE: To demonstrate 4.7 Tesla (T) imaging methods for visualizing lesions in multiple sclerosis in the human brain using phase susceptibility-weighting and T2 weighting. MATERIALS AND METHODS: Seven patients with relapsing-remitting multiple sclerosis were imaged at 4.7T using three-dimensional (3D) susceptibility-weighted imaging (SWI) with 0.90 mm(3) voxel volumes, and with 2D T2-weighted fast spin echo (T2WFSE) with 0.34 mm(3) voxels and 1.84 mm(3) voxels. The visibility of MS lesions at 4.7T with phase SWI and T2WFSE was assessed by independent lesion counts made by an experienced neuroradiologist, and by quantitative measures. RESULTS: High resolution T2WFSE at 4.7T provided excellent depiction of hyperintense lesions. When combined with phase SWI, 124 total lesions were identified of which 18% were only visible on phase SWI and not on T2WFSE. The phase lesions had a mean phase shift relative to local background of -11.15 +/- 5.97 parts per billion. CONCLUSION: Imaging at 4.7T can provide both high quality, high resolution T2WFSE and SWI for visualization of lesions in multiple sclerosis. Phase susceptibility-weighting can identify additional lesions that are not visible with high resolution T2WFSE.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".