Applying a [<sup>11</sup>C]raclopride template to automated binding potential estimation in HRRT brain PET
Bibliographic record
Abstract
Performing kinetic analysis of [11C]raclopride PET data typically involves user intervention to identify key volumes of interest, such as the cerebellum for the reference region and the caudate and putamen for regions where the binding potential (BP) needs to be estimated. In many PET centres, this process is neither automated nor standardized, possibly producing discrepancies between centres. Conventionally, MR anatomical images are used to identify the key volumes of interest, but this is difficult to automate robustly, and user intervention can sometimes be required. This work considers instead the use of an anatomically labeled [11C]raclopride template, formed from multiple subjects, which has the key advantages of low noise, good resolution and having a highly similar spatiotemporal intensity distribution to any given single subject raclopride scan. This makes the template an excellent target for automated image registration and segmentation. We present a methodology which works on post-reconstruction images, demonstrating an automated and consistent way of identifying key regions of interest (ROIs) and determining binding potential without any MR image or user intervention. The performance of the methodology is evaluated using simulated and real [11C]raclopride data. The simplified reference tissue model with the basis function method (SRTM-BFM) was used for the kinetic modeling.
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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 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".