Symmetric geometric transfer matrix partial volume correction for PET imaging: principle, validation and robustness
Bibliographic record
Abstract
Limited spatial resolution of positron emission tomography (PET) often requires partial volume correction (PVC) to improve the accuracy of quantitative PET studies. Conventional region-based PVC methods use co-registered high resolution anatomical images (e.g. computed tomography (CT) or magnetic resonance images) to identify regions of interest. Spill-over between regions 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 relies on spill-over between RSFs rather than between regions. It is shown that the sGTM is mathematically equivalent to Labbe's method; however it is a region-based method rather than a voxel-based method and it avoids handling large matrices. The sGTM method was validated using two three-dimensional (3D) digital phantoms and one physical phantom. A 3D digital sphere phantom with sphere diameters ranging from 5 to 30 mm and a sphere-to-background uptake ratio of 3-to-1 was used. A 3D digital brain phantom was used with four different anatomical regions and a background region with different activities assigned to each region. A physical sphere phantom with the same geometry and uptake as the digital sphere phantom was manufactured and PET-CT images were acquired. Using these three phantoms, the performance of the sGTM method was assessed against that of the GTM method in terms of accuracy, precision, noise propagation and robustness. The robustness was assessed by applying mis-registration errors and errors in estimates of PET point spread function (PSF). In all three phantoms, the results showed that the sGTM method has accuracy similar to that of the GTM method and within 5%. 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 mis-registration errors or errors in estimates of PSF occur. The improved robustness was more pronounced for smaller objects. In conclusion, the sGTM method was analytically derived and validated. The noise characteristics and robustness of the sGTM method were better than the conventional GTM method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.019 |
| 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 | 0.001 |
| 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 | 0.001 |
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".