Assessment of dual time point protocols to produce parametric <i>K</i><sub>i</sub> images in FDG PET/CT: A virtual clinical study
Bibliographic record
Abstract
Abstract Purpose This simulation study investigated the feasibility of generating Patlak Ki images using a dual time point (DTP‐Ki) scan protocol involving two 3‐min/bed routine static PET scans and, subsequently, assessed DTP‐Ki performance for an optimal DTP scan time frame combination, against conventional Patlak Ki estimated from complete 0–93 min dynamic PET data. Methods Six realistic heterogeneous tumors of different characteristic spatiotemporal [18F]FDG uptake distributions for three noise levels commonly found in clinical studies and 20 noise realizations (N = 360 samples) were produced by analytic simulations of the XCAT phantom. Subsequently, DTP‐Ki images were generated by performing standard linear indirect Patlak analysis with t*‐min (Patlakt* = 12) using a scaled population‐based input function (sPBIF) model on 66 combinations of early and late 3‐min/bed static whole‐body PET reconstructed images. All DTP‐Ki images were evaluated against respective DTP‐Ki images estimated with Patlakt* = 12 and 0–93 min individual input functions (iIFs) and against gold standard Ki images estimated with Patlakt* = 12, 0–93 min iIFs and tissue time activity curves from all reconstructed WB passes 12–93 min post injection. The optimal combination of early and late frames, in terms of attaining the highest correlation between DTP‐Ki with sPBIF and gold standard Ki was also determined from a set of 66 different combinations of 2‐min early and late frames. Moreover, the performance of DTP‐Ki with sPBIF was compared against that of the retention index (RI) in terms of their correlation to the gold standard Ki. Finally, the feasibility and practicality of DTP protocol in the clinic were assessed through the analysis of nine patients. Results High correlations (>0.9) were observed between DTP‐Ki values from sPBIF and those from iIFs for all evaluated DTP protocols while the mean AUC difference between sPBIF and iIFs was less than 10%. The percentage difference of mean values between DTP‐Ki from sPBIF and from iIFs was less than 1%. DTP Ki from sPBIF exhibited significantly higher correlation with gold standard Ki, in contrast to RI, across all 66 DTP protocols (p < 0.05 using the two‐tailed t‐test by Williams) with the highest correlation attained for the 50‐53‐min early + 90‐93‐min late scan time frames (optimal DTP protocol). Conclusion Feasibility of generating Patlak Ki [18F] FDG images from an early and a late post injection 3‐min/bed routine static scan using a population‐based input function model was demonstrated and an optimal DTP scan protocol was determined. The results indicated high correlations between DTP‐Ki and gold‐standard Ki images that are significantly larger than those between RI and gold‐standard Ki.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".