Optimization of the reference region method for dual pharmacokinetic modeling using Gd‐DTPA/MRI and <sup>18</sup>F‐FDG/PET
Bibliographic record
Abstract
PURPOSE: The combination of MRI and positron emission tomography (PET) offers new possibilities for the development of novel methodologies. In pharmacokinetic image analysis, the blood concentration of the imaging compound as a function of time, [i.e., the arterial input function (AIF)] is required for MRI and PET. In this study, we tested whether an AIF extracted from a reference region (RR) in MRI can be used as a surrogate for the manually sampled (18) F-FDG AIF for pharmacokinetic modeling. METHODS: An MRI contrast agent, gadolinium-diethylenetriaminepentaacetic acid (Gd-DTPA) and a radiotracer, (18) F-fluorodeoxyglucose ((18) F-FDG), were simultaneously injected in a F98 glioblastoma rat model. A correction to the RR AIF for Gd-DTPA is proposed to adequately represent the manually sampled AIF. A previously published conversion method was applied to convert this AIF into a (18) F-FDG AIF. RESULTS: The tumor metabolic rate of glucose (TMRGlc) calculated with the manually sampled (18) F-FDG AIF, the (18) F-FDG AIF converted from the RR AIF and the (18) F-FDG AIF converted from the corrected RR AIF were found not statistically different (P>0.05). CONCLUSION: An AIF derived from an RR in MRI can be accurately converted into a (18) F-FDG AIF and used in PET pharmacokinetic modeling.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".