Direct 4D PET MLEM reconstruction of parametric images using the simplified reference tissue model with the basis function method for [<sup>11</sup>C]raclopride
Bibliographic record
Abstract
This work assesses the one-step late maximum likelihood expectation maximization (OSL-MLEM) 4D PET reconstruction algorithm for direct estimation of parametric images from raw PET data when using the simplified reference tissue model with the basis function method (SRTM-BFM) for the kinetic analysis. To date, the OSL-MLEM method has been evaluated using kinetic models based on two-tissue compartments with an irreversible component. We extend the evaluation of this method for two-tissue compartments with a reversible component, using SRTM-BFM on simulated 3D + time data sets (with use of [(11)C]raclopride time-activity curves from real data) and on real data sets acquired with the high resolution research tomograph. The performance of the proposed method is evaluated by comparing voxel-level binding potential (BPND) estimates with those obtained from conventional post-reconstruction kinetic parameter estimation. For the commonly chosen number of iterations used in practice, our results show that for the 3D + time simulation, the direct method delivers results with lower (%)RMSE at the normal count level (decreases of 9-10 percentage points, corresponding to a 38-44% reduction), and also at low count levels (decreases of 17-21 percentage points, corresponding to a 26-36% reduction). As for the real 3D data set, the results obtained follow a similar trend, with the direct reconstruction method offering a 21% decrease in (%)CV compared to the post reconstruction method at low count levels. Thus, based on the results presented herein, using the SRTM-BFM kinetic model in conjunction with the OSL-MLEM direct 4D PET MLEM reconstruction method offers an improvement in performance when compared to conventional post reconstruction methods.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".