Noninvasive Quantification of Local Cerebral Metabolic Rate of Glucose for Clinical Application Using Positron Emission Tomography and <sup>18</sup> F-Fluoro-2-Deoxy- <scp>d</scp> -Glucose
Bibliographic record
Abstract
Until now, input function is still required in quantification of local cerebral metabolic rate of glucose (LCMRGlc) using positron emission tomography (PET) and (18)F-fluoro-2-deoxy-D-glucose (FDG). Some image-derived methods were developed to get input function noninvasively instead of the arterial blood sampling method, but they needed to make complicated corrections manually, so they cannot always be applied in clinic directly. Here, we propose a simple method based on the Patlak approach by using a reference tissue region and without using any information of input function. This simulation study revealed that the present method was in good agreement with Patlak method; the difference between two methods was less than 5%. The statistical errors with two methods were also obtained, and the results showed the accuracy of LCMRGlc estimated with present method was better than that with Patlak method slightly. The simulation results indicated that the calculation of LCMRGlc with present method was quite stable and independent of the choosing of reference tissue region. All of these show that the present method is a good approximation to Patlak method. The calculation with this method is very simple and easy to perform voxel by voxel; therefore, it can be widely used not only in laboratory studies but also in clinical applications although it only provides the relative rates.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".