Impact of DL HYPR4D Kernel Method on Detection of Task-Induced Neurotransmitter Release
Bibliographic record
Abstract
We describe an investigation on the impact of the recently proposed deep learning (DL) assisted HYPR4D kernel method (DL HYPR4D-K) on detection of task-induced neurotransmitter release which is highly sensitive to the 4D PET image quality. Human $\left[{ }^{11} \mathrm{C}\right]$ RAC data (a subject with Parkinson’s Disease) with $\sim 15 \mathrm{mCi}$ bolus injection were acquired on GE SIGNA PET/MR for 71 mins. A finger tapping task was introduced at 36 mins post injection. The acquired dynamic data were reconstructed using standard HYPR4D kernel method and DL HYPR4D kernel method. IHYPR4D post processing was applied to the images reconstructed with standard HYPR4D kernel method to further reduce noise (i.e. HYPR4D-K + IHYPR4D). A recently proposed Residual Space Detection (RSD) method was applied to the images reconstructed with DL HYPR4D-K and HYPR4D-K + IHYPR4D to produce neurotransmitter release maps as well as histograms of release amplitudes. Simulations were also performed to see if the trend observed from human data agrees with simulated results. It was observed that better preservation of release amplitude (~ 60%) can be achieved by the DL HYPR4D-K as compared to HYPR4D-K + IHYPR4D, and this resulted in additional detection of voxels likely with release. In addition, DL HYPR4D-K enables detections of smaller clusters of release and clusters with lower release amplitudes compared to HYPR4D-K + IHYPR4D.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".