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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".