HYPR4D Kernel Method with an Unsupervised 2.5SD+0.5TD Deep Learning Assisted Kernel Matrix
Bibliographic record
Abstract
We describe a deep learning (DL) assisted HYPR4D kernelized reconstruction which produces nearly noise-free voxel level time-activity-curves (TACs) while preserving quantification within small structures as well as consistent spatiotemporal patterns/features within measured data. A series of iteration specific unsupervised DL networks was trained to minimize the inconsistency between the 4D kernelized OSEM subset estimates so that the network output would contain the most consistent features over the subsets for single subject training. We then construct the proposed DL HYPR4D kernel matrix based on the 4D high frequency features extracted from the network output for each reconstruction iteration. Moreover, inspired by the standard 2.5D de-noising models which use 3 slice input, we further include the corresponding slices one time frame before and after the frame of interest to provide spatiotemporal noise reduction (i.e. 2.5SD+0.5TD). Finally, we introduce a final tuning step within the reconstruction to mitigate the typical over-smoothing observed from the network output to preserve the quantification within small target structures. Contrast phantom and human [18F]FDG data acquired on GE SIGNA PET/MR were used for evaluation. The proposed DL HYPR4D kernel method outperformed the standard HYPR4D kernel method as well as TOF-OSEM and TOF-BSREM (Q.Clear) in terms contrast recovery vs noise. The proposed final tuning reduced the underestimation bias due to over-smoothing within a 4mm target structure from ~15% to < 2% while maintaining nearly noise-free voxel level TACs. In addition, the proposed unsupervised DL assisted reconstruction also outperformed the supervised DL version in terms of minimizing biased patterns along the TACs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".