Preliminary Assessment of Reference Region Quantification and Reduced Scanning Times for [ <sup>18</sup> F]SynVesT-1 PET in Parkinson’s Disease
Bibliographic record
Abstract
Synaptic density in the central nervous system can be measured in vivo using PET with [ 18 F]SynVesT-1. While [ 18 F]SynVesT-1 has been proven to be a powerful radiopharmaceutical for PET imaging of neurodegenerative disorders such as Parkinson’s disease (PD), its currently validated acquisition and quantification protocols are invasive and technically challenging in these populations due to the arterial sampling and relatively long scanning times. The objectives of this work were to evaluate a noninvasive (reference tissue) quantification method for [ 18 F]SynVesT-1 in PD patients and to determine the minimum scan time necessary for accurate quantification. [ 18 F]SynVesT-1 PET scans were acquired in 5 patients with PD and 3 healthy control subjects for 120 min with arterial blood sampling. Quantification was performed using the one-tissue compartment model (1TCM) with arterial input function, as well as with the simplified reference tissue model (SRTM) to estimate binding potential ([Formula: see text]) using centrum semiovale (CS) as a reference region. The SRTM2 method was used with [Formula: see text] fixed to either a sample average value (0.037 min -1 ) or a value estimated first through coupled fitting across regions for each participant. Direct SRTM estimation and the Logan reference region graphical method were also evaluated. There were no significant group differences in CS volume, radiotracer uptake, or efflux ([Formula: see text]). Each fitting method produced [Formula: see text] estimates in close agreement with those derived from the 1TCM (subject [Formula: see text], [Formula: see text]), with no difference in bias between the control and PD groups. With SRTM2, [Formula: see text] estimates from truncated scan data as short as 80 min produced values in excellent agreement with the data from the full 120 min scans ([Formula: see text]). While these are preliminary results from a small sample of patients with PD ([Formula: see text]), this work suggests that accurate synaptic density quantification may be performed without blood sampling and with scan time under 90 minutes. If further validated, these simplified procedures for [ 18 F]SynVesT-1 PET quantification can facilitate its application as a clinical research imaging technology and allow for larger study samples and include a broader scope of patients including those with neurodegenerative diseases.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".