SU‐E‐I‐173: Factor Analysis with Prior Information Using Projected Gradient Method — Application to 11C‐DTBZ Dynamic PET Dataset for Early Detection of Parkinsonˈs Disease
Bibliographic record
Abstract
Purpose: To apply factor analysis technique to a sequence of 11C‐DTBZ dynamic PET images to healthy and diseased subjects in order to extract factor curves (or time activity curves) and associated factor images and develop a metric to detect extent of Parkinsonˈs disease. Methods: Philips Gemini (C) PET/CT scanner (with 4mm isotropic voxels) is used to collect a dynamic dataset consisting of a time sequence of 16 frames (with unequal temporal spacing) of the brain from healthy and diseased subjects. Several image preprocessing techniques (e.g. noise reduction using singular value decomposition, voxel‐averaging) are used on these dynamic datasets prior to implementing factor analysis using projected gradient method in the framework of non‐negative matrix factorization to extract physiologically meaningful structures. A priori information (region‐of‐interest based time activity curves) is used to warm start the optimization process in order to reduce the number of possible solutions. Results: The factor analysis technique separates each dynamic dataset into two time‐dynamic factors — one factor represents the striatum (directly related to the disease) while the other factor represents the non‐striatum tissues. The factor curves and images related to the former show clear difference in the tracer uptake among the healthy and diseased subjects. Our metric, which relies on such difference, indicates that the tracer uptake in striatum tissue for the healthy subject is roughly four times higher than that of the diseased subject. Conclusions: This study suggests that the technique can decompose large dynamic datasets into parts‐based images (or volumes) that represent the underlying physiological structures, and has the potential to significantly aid in the review process for evaluating dynamic datasets by clinicians. The technique is not limited to dynamic PET images and can be applied to any sequence of dynamic images.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| 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".