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Record W4385755562 · doi:10.1155/2023/1855985

Preliminary Assessment of Reference Region Quantification and Reduced Scanning Times for [ <sup>18</sup> F]SynVesT-1 PET in Parkinson’s Disease

2023· article· en· W4385755562 on OpenAlex
Kelly Smart, Carme Uribe, Kimberly L. Desmond, Sarah L. Martin, Neil Vasdev, Antonio P. Strafella

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMolecular Imaging · 2023
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health NetworkCentre for Addiction and Mental Health
FundersH2020 Marie Skłodowska-Curie ActionsCanadian Institutes of Health ResearchHorizon 2020 Framework ProgrammeCanada Research ChairsEuropean CommissionAzrieli FoundationCanada Foundation for InnovationWeston Brain InstituteMichael J. Fox Foundation for Parkinson's Research
KeywordsNuclear medicineMedicinePhysics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.362
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it