Evaluation of Image Quality Improvements When Adding Patient Outline Constraints into a Generalized Scatter PET Reconstruction Algorithm
Bibliographic record
Abstract
Scattered coincidences degrade image contrast and compromise quantitative accuracy in positron emission tomography (PET). A number of approaches to estimating and correcting scattered coincidences have been proposed, but most of them are based on estimating and subtracting a scatter sinogram from the measured data. We have previously shown that both true and scattered coincidences can be treated similarly by using Compton scattering kinematics to define a locus of scattering which may in turn be used to reconstruct the activity distribution using a generalized scatter maximum-likelihood expectation maximization (GS-MLEM) algorithm. The annihilation position can be further confined by taking advantage of the patient outline (or a geometrical shape that encompasses the patient outline). The proposed method was tested on a phantom generated using GATE. The results have shown that for scatter fractions of 10–60% this algorithm improves the contrast recovery coefficients (CRC) by 4 to 28.6% for a source and 5.1 to 40% for a cold source while the relative standard deviation (RSD) was reduced. Including scattered photons directly into the reconstruction eliminates the need for (often empirical) scatter corrections, and further improvements in the contrast and noise properties of the reconstructed images can be made by including the patient outline in the reconstruction algorithm as a constraint.
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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 | 0.005 | 0.017 |
| 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 | 0.000 |
| 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".