Voxel level quantification of [11C]CURB, a radioligand for Fatty Acid Amide Hydrolase, using high resolution positron emission tomography
Bibliographic record
Abstract
[11C]CURB is a novel irreversible radioligand for imaging fatty acid amide hydrolase in the human brain. In the present work, we validate an algorithm for generating parametric map images of [11C]CURB acquired with a high resolution research tomograph (HRRT) scanner. This algorithm applies the basis function method on an irreversible two-tissue compartment model (k4 = 0) with arterial input function, i.e., BAFPIC. Monte Carlo simulations are employed to assess bias and variability of the binding macroparameters (Ki and λk3) as a function of the voxel noise level and the range of basis functions. The results show that for a [11C]CURB time activity curve with noise levels corresponding to a voxel of an image acquired with the HRRT and reconstructed with the filtered back projection algorithm, the implementation of BAFPIC requires the use of a constant vascular fraction of tissue (5%) and a cutoff for slow frequencies (0.06 min-1). With these settings, BAFPIC maintains the probabilistic distributions of the binding macroparameters with approximately Gaussian shape and minimizes the bias and variability for large physiological ranges of the rate constants of [11C]CURB. BAFPIC reduces the variability of Ki to a third of that given by Patlak plot, the standard graphical method for irreversible radioligands. Application to real data demonstrated an excellent correlation between region of interest and BAFPIC parametric data and agreed with the simulations results. Therefore, BAFPIC with a constant vascular fraction can be used to generate parametric maps of [11C]CURB images acquired with an HRRT provided that the limits of the basis functions are carefully selected.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".