Conversion of arterial input functions for dual pharmacokinetic modeling using Gd‐DTPA/MRI and <sup>18</sup>F‐FDG/PET
Bibliographic record
Abstract
Reaching the full potential of magnetic resonance imaging (MRI)-positron emission tomography (PET) dual modality systems requires new methodologies in quantitative image analyses. In this study, methods are proposed to convert an arterial input function (AIF) derived from gadolinium-diethylenetriaminepentaacetic acid (Gd-DTPA) in MRI, into a (18)F-fluorodeoxyglucose ((18)F-FDG) AIF in PET, and vice versa. The AIFs from both modalities were obtained from manual blood sampling in a F98-Fisher glioblastoma rat model. They were well fitted by a convolution of a rectangular function with a biexponential clearance function. The parameters of the biexponential AIF model were found statistically different between MRI and PET. Pharmacokinetic MRI parameters such as the volume transfer constant (K(trans)), the extravascular-extracellular volume fraction (ν(e)), and the blood volume fraction (ν(p)) calculated with the Gd-DTPA AIF and the Gd-DTPA AIF converted from (18)F-FDG AIF normalized with or without blood sample were not statistically different. Similarly, the tumor metabolic rates of glucose (TMRGlc) calculated with (18) F-FDG AIF and with (18) F-FDG AIF obtained from Gd-DTPA AIF were also found not statistically different. In conclusion, only one accurate AIF would be needed for dual MRI-PET pharmacokinetic modeling in small animal models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".