Dual-Tracer Imaging on a Long–Axial-Field-of-View PET: A Proof-of-Principle Study with [<sup>18</sup>F]FGln and [<sup>18</sup>F]FDG
Bibliographic record
Abstract
Current protocols necessitate imaging 2 18F-labeled tracers in separate sessions. Herein, we report on the development and testing of a protocol that sequentially images 2 18F-labeled tracers—18F-(2S,4R)4-fluoroglutamine, commonly known as [18F]FGln, and [18F]FDG—in a single session on a long–axial-field-of-view PET scanner to study cancer metabolism. Methods: A single [18F]FGln scan was used to estimate the minimal injected activity and scan duration to calculate an accurate volume of distribution. This was followed by a dual-tracer study in a second patient with a low-dose (41-MBq), shortened (29-min) [18F]FGln scan, followed by a full-dose (394-MBq) [18F]FDG scan for 60 min. Protocol performance was assessed using the resulting kinetic parameters. Results: [18F]FGln subsampling demonstrated stable estimates of volume of distribution with a scan duration of 30 min and a dose of 37 MBq. The dual-tracer [18F]FDG image obtained 60 min after injection was of diagnostic quality, with minimal (6%) residual [18F]FGln signal. Conclusion: This proof-of-principle dual-tracer study demonstrated the feasibility of a [18F]FGln/[18F]FDG protocol that leveraged the high sensitivity of long–axial-field-of-view PET to inject the first tracer at a low dose and the second tracer at a greater dose, overwhelming the signal from the first tracer.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".