A Strategy for Obtaining an Accurate Image Derived Input Function in Dynamic Brain FDG PET
Bibliographic record
Abstract
We describe a multi-aspect strategy to obtain an accurate image derived input function (IDIF) in dynamic brain FDG PET. We first investigate the accuracy of the scatter correction in low count/short temporal frames as it impacts the limits of the initial temporal resolution. Then we propose a simple Maximum Intensity Projection (MIP) based voxel search with and without coupling to information obtained from venous samples to extract the IDIF from images reconstructed using our prior-free HYPR4D denoised kernel method with spatially variant resolution modeling and a truly four dimensional feature vector. Using human subject scans, we show that the proposed method produces comparable IDIF as compared to venous sampling after the peak with the peak magnitude within the typically observed range. Moreover, the MIP voxels from the PSF-HYPR4D denoised kernel method were found to match the time course of venous samples better than standard PSF-TOFOSEM with and without post filter. Further validations will be performed with more subjects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".