Comparison between an image- and a sinogram-based correction algorithm for partial volume effect in 3D PET imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Two fully 3D partial volume correction (PVC) techniques in PET imaging are compared. They follow the region based method proposed in 2D by O. Rousset et al. (1998). They assume that the object being imaged consists of anatomical domains with homogeneous true activity and that the voxel intensity in the PET image is the sum of the true activity in each domain weighted by its regional spread function (RSF). The two implementations that we compare differ in the way the RSFs are obtained: (1) a 3D extension of the original work of Rousset, that is based on an analytical simulator, and (2) a convolution of the anatomical tissue domains, in the image space, with the 3D PET system PSF. We used a Monte Carlo simulated cerebral dynamic study to assess the performance of both PVC implementations in the recovery of the time activity curves for the striata. The two methods allow the recovery of the true time activity curves with RMS errors of about 4%. The advantage of the second approach is its simplicity and rapidity that would enable fully 3D PVC in a clinical context, for protocols dedicated to compartmental analysis that require a few accurate ROI time activity curves.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it