Method of simulation and visualization of FDG metabolism based on VHP image
Bibliographic record
Abstract
FDG ([18F] 2-fluoro-2-deoxy-D-glucose) is the typical tracer used in clinical PET (positron emission tomography) studies. The FDG-PET is an important imaging tool for early diagnosis and treatment of malignant tumor and functional disease. The main purpose of this work is to propose a method that represents FDG metabolism in human body through the simulation and visualization of 18F distribution process dynamically based on the segmented VHP (Visible Human Project) image dataset. First, the plasma time-activity curve (PTAC) and the tissues time-activity curves (TTAC) are obtained from the previous studies and the literatures. According to the obtained PTAC and TTACs, a set of corresponding values are assigned to the segmented VHP image, Thus a set of dynamic images are derived to show the 18F distribution in the concerned tissues for the predetermined sampling schedule. Finally, the simulated FDG distribution images are visualized in 3D and 2D formats, respectively, incorporated with principal interaction functions. As compared with original PET image, our visualization result presents higher resolution because of the high resolution of VHP image data, and show the distribution process of 18F dynamically. The results of our work can be used in education and related research as well as a tool for the PET operator to design their PET experiment program.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".