Sci—Fri AM: Imaging — 07: Symmetric geometric transfer matrix partial volume correction technique for emission tomography: Principle, validation, and robustness
Bibliographic record
Abstract
Partial volume correction (PVC) is often needed to correct for limited spatial resolution in quantitative Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) studies. In conventional region-based PVC methods, spill over between regions segmented from coregistered computed tomography (CT) or magnetic resonance (MR) images is accounted for by calculating regional spread functions (RSFs) in a geometric transfer matrix (GTM) framework. This paper describes a new analytically derived symmetric GTM (sGTM) method that considers spill over between RSFs rather than between regions. The sGTM is mathematically equivalent to Labbe's method, however it is region-based rather than voxel-based and it avoids handling large matrices. The sGTM method was validated using an MR-based 3D digital brain phantom and a physical phantom containing spheres 5 mm to 30 mm in diameter. The sGTM method was compared to the GTM method in terms of accuracy, precision, noise propagation, and robustness, i.e. effects of mis-registration or point spread function (PSF) estimation errors. The results showed that the sGTM method has accuracy similar to that of the GTM method, and within 5% of the true value. However, the sGTM method showed better precision and noise propagation than the GTM method, especially for spheres smaller than 13 mm. Moreover, the sGTM method was more robust than the GTM method when misregistration or errors in estimates of PSF occurred. In conclusion, the sGTM method was analytically derived and validated and shown to exhibit better noise characteristics and robustness compared to the GTM method.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.035 |
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".