Direct 4D PET MLEM reconstruction of parametric images using the simplified reference tissue model with the basis function method
Bibliographic record
Abstract
The purpose of this work is to assess the one-step late maximum likelihood expectation maximization (OSL-MLEM) direct 4D PET reconstruction algorithm when using the simplified reference tissue model with the basis function method (SRTM-BFM). To date, the OSL-MLEM method has been evaluated using kinetic models based on two-tissue compartments with an irreversible component. We therefore extend the evaluation of this method for two-tissue compartments with a reversible component, using SRTM-BFM on simulated 2D and 3D + time data sets (with use of [11C]raclopride time-activity curves (TACs) from real data) and on real data sets acquired with the high resolution research tomograph (HRRT). Furthermore, this work investigates the impact of correcting the TACs by the frame length, as assumed by most conventional kinetic parameter estimation techniques (applied post-reconstruction) used in practice. The performance of the proposed method is evaluated by comparing binding potential (BP) estimates with those obtained from conventional post-reconstruction kinetic parameter estimation. It is shown that, for the 2D + time simulation, SRTM-BFM within the OSL-MLEM framework delivers lower%BIAS and%CV, and thus lower%RMSE, in BP estimates compared to the post reconstruction approach, while for the real 3D data set the method delivers lower spatial%CV, in addition to better BP parametric image quality, when using resolution modeling. Finally, frame length correction can be applied but correct weighting is necessary to obtain the best performance.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".