Detecting Beta-Amyloid Plaque via Low Rank Based Orthogonal Projection and Spatial-Spectrum Detector Using High-Resolution Quantitative Susceptibility Mapping for Preclinical Studies
Bibliographic record
Abstract
Detecting beta-amyloid (A$\beta$) plaques at different stages is crucial for accurate assessment and effective intervention in Alzheimer's disease (AD). In this study, we developed a novel method for reliably identifying A$\beta$ plaques, characterized by sparse negative susceptibility values, in preclinical studies using high-resolution quantitative susceptibility mapping (QSM), named QSM-PLAQUE A$\beta$ Detector. This approach decomposes a high-resolution QSM MRI image into three components: L (representing the background subspace), S (representing the signals subspace), and N (representing the noise). Subsequently, we established an orthogonal subspace based on L to eliminate the background from the sum of L and S. Finally, a plaque detection process was conducted, where A$\beta$ plaques were identified based on the neighbor spectrum (NS) of a voxel being tested rather than just analyzing the voxel itself alone. Experiments demonstrated that the proposed method effectively detects A$\beta$ plaques of varying shapes and intensities across the entire mouse brain. It shows robust performance across histology, high-resolution QSM MRI, and synthesized datasets, without requiring training samples. The QSM-PLAQUE A$\beta$ Detector provides a practical framework for identifying and visualizing A$\beta$ plaques in preclinical studies, offering a new strategy for quantitative assessment of A$\beta$ plaques and may guide the development of advanced techniques for preclinical AD research.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".