Characterization of tomographic sampling in Hybrid PET using the Fourier crosstalk matrix
Bibliographic record
Abstract
Hybrid positron emission tomography (PET) cameras can be used to measure the distribution of positron emitting radionuclides. An important system parameter for Hybrid PET is the appropriate tomographic sampling requirements. In this paper, a previously developed theoretical formulation for quantifying sampling in continuous-to-discrete tomographic systems, termed the "crosstalk matrix," is used to provide information on the recoverability of the Fourier coefficients that represent the continuous object. In addition, the crosstalk matrix can be related to image quality assessment. Here, we use the crosstalk matrix to evaluate tomographic sampling for Hybrid PET systems. Dual-and triple-head systems were compared, with emphasis placed on studying how system performance changes as the number of gantry stops is increased, and as the line-of-response acceptance angle is reduced. Examination of the crosstalk matrix, as well as figures-of-merit measuring task performance that are computed using the crosstalk matrix, show that increasing angular sampling improves Fourier coefficient recoverability and reduces aliasing.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".