Implementation of Scatter Corrected List-Mode OP-EM Reconstruction Algorithm and a Dual (Histogram/List Mode) Reconstruction Scheme for Dynamic PET Imaging
Bibliographic record
Abstract
We describe an implementation of ordinary Poisson list-mode expectation maximization (OP-LMEM) algorithm for the high resolution research tomography (HRRT) with a scatter correction method based on the single scatter simulation (SSS) technique, and a random correction method based on the variance-reduced delayed-coincidence technique. A hybrid EM algorithm has also been implemented in list-mode reconstruction (H-LMEM) using delayed-coincidence event subtraction technique with the same scatter correction as in OP-LMEM. The reconstructed images of a dynamic scanning sequence of a contrast phantom have been compared with those reconstructed using the histogram-mode reconstruction, in particular, 3D ordinary Poisson ordered subset expectation maximization (3D-OP) with the same variance-reduced random and estimated scatter. The transaxial and axial profile analyses have shown excellent agreement between histogram and both list-mode reconstructions. Likewise the preliminary contrast and noise analyses have shown a close agreement between histogram-mode and both list-mode reconstructions. Based on these results, a dual reconstruction scheme can now be applied to dynamic imaging in positron emission tomography (PET) with scatter correction; i.e. histogram-mode reconstruction (3D-OP) can be applied to frames with a large number of counts, and list-mode reconstruction (OP-LMEM) will subsequently be used for low statistics frames, as an effort to obtain efficient and quantitatively accurate reconstructions applicable to state-of-the-art dynamic PET imaging using the HRRT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".