Feature Space-Guided Denoising of Noisy 4-D Data: Applications to Dynamic PET Imaging and Dual-Calibrated Functional MRI
Bibliographic record
Abstract
4D neuroimaging methods, including dynamic PET and functional MRI, capture the spatiotemporal behaviour of physiological processes. Subsequent analysis, such as kinetic modeling of dynamic PET data, can provide parametric images related to unique aspects of physiology. However, 4D data is often exceptionally noisy, thus requiring post-processing denoising for reliable quantitative results. Several proposed 4D denoising algorithms reduce noise via signal averaging of physiologically similar voxels, but often have the trade-off of reduced accuracy. Furthermore, many do not fully exploit non-local voxel similarity, due to computational constraints and/or a suboptimal heuristic for identifying physiological similarity. In this work, we propose a denoising framework that uses a low-dimensional feature space representation of the data to identify similar voxels. This permits targeted non-local denoising to produce an initial denoised product, which is then passed to the HighlY constrained backPRojection (HYPR) algorithm to ensure data consistency. We optimize the feature space for data from different PET tracers and demonstrate cross-modality applicability, using dual-calibrated fMRI as a proof-of-concept. Additionally, we show our proposed method is superior to comparable denoising algorithms in terms of quantitative accuracy and precision of parametric images computed from the denoised data—thus demonstrating improvements relevant for research and clinical applications.
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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 | 0.001 |
| 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 | 0.000 |
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".