Detector response models for statistical iterative image reconstruction in high resolution PET
Bibliographic record
Abstract
One limitation in a practical implementation of statistical iterative image reconstruction is to compute a transition matrix accurately modeling the relationship between projection and image spaces. Detector response function (DRF) in positron emission tomography (PET) is broad and spatially-variant, leading to large transition matrices taking too much space to store. In this work, the authors investigate the effect of simpler DRF models on image quality in maximum likelihood expectation maximization reconstruction. The authors studied 6 cases of modeling projection/image relationship: tube/pixel geometric overlap with tubes centered on detector face; same as previous with tubes centered on DRF maximum; two different fixed-width Gaussian functions centered on DRF maximum weighing tube/pixel overlap; same as previous with a Gaussian of the same spectral resolution as DRF; analytic DRF based on linear attenuation of /spl gamma/-rays in detector arrays weighing tube/pixel overlap. The authors found that DRF oversimplification may affect visual image quality and image quantification dramatically, including artefact generation. They showed that analytic DRF yielded images of excellent quality for a small animal PET system with long, narrow detectors and generated a transition matrix for 2-D reconstruction that could be easily fitted into the memory of current stand-alone computers.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".