Neuromelanin‐Sensitive Magnetic Resonance Imaging Using DANTE Pulse
Bibliographic record
Abstract
BACKGROUND: Neuromelanin-sensitive magnetic resonance imaging techniques have been developed but currently require relatively long scan times. The aim of this study was to assess the ability of black-blood delay alternating with nutation for tailored excitation-prepared T1-weighted variable flip angle turbo spin echo (DANTE T1-SPACE), which provides relatively high resolution with a short scan time, to visualize neuromelanin in the substantia nigra pars compacta (SNpc). METHODS: Participants comprised 49 healthy controls and 25 patients with Parkinson's disease (PD). Contrast ratios of SNpc and hyperintense SNpc areas, which show pixels brighter than thresholds, were assessed between DANTE T1-SPACE and T1-SPACE in healthy controls. To evaluate the diagnostic ability of DANTE T1-SPACE, the contrast ratios and hyperintense areas were compared between healthy and PD groups, and receiver operating characteristic analyses were performed. We also compared areas under the curve (AUCs) between DANTE T1-SPACE and the previously reported gradient echo neuromelanin (GRE-NM) imaging. Each analysis was performed using original images in native space and images transformed into Montreal Neurological Institute space. Values of P < 0.05 were considered significant. RESULTS: DANTE T1-SPACE showed significantly higher contrast ratios and larger hyperintense areas than T1-SPACE. On DANTE T1-SPACE, healthy controls showed significantly higher contrast ratios and larger hyperintense areas than patients with PD. Hyperintense areas in native space analysis achieved the best AUC (0.94). DANTE T1-SPACE showed AUCs as high as those of GRE-NM. CONCLUSIONS: DANTE T1-SPACE successfully visualized neuromelanin of the SNpc and showed potential for evaluating PD. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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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) | 0.001 | 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".