MR fingerprinting for quantification of brain amyloid burden: from development to prospective multi-site external validation
Bibliographic record
Abstract
Motivation: A non-invasive amyloid beta (Aβ) imaging technique is needed for objective diagnosis and treatment monitoring of Alzheimer’s disease. Goal(s): To develop and validate an MRF-based method quantifying brain Aβ. Approach: A framework with efficient MRF data acquisition, neural network decoding, and atlas-based segmentation was implemented. A prospective analysis was conducted on external dataset to evaluate its generalizability, repeatability, and correlation with Aβ-PET measurements and clinical cognitive function tests. Results: The method showed high repeatability (CV<2%), significant correlation with Aβ-PET measurements and Montreal Cognitive Assessment test (p=0.015 and 0.020, respectively), and discriminated subject-level Aβ positivity with an AUC of 0.84 on external test set. Impact: The proposed framework is compatible with clinical 3T MRI and offers ‘one-stop’ examination in 10 minutes for patients with cognitive decline by providing structural MRI and Aβ-quantification. Its non-invasive nature facilitates longitudinal evaluation and correlates with Aβ-PET and cognitive function.
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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 | 0.022 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".