Simplified Workflow using a 20 min Dynamic 18F‐Florbetaben PET Scan to Measure Cerebral Blood Flow and Binding Potential
Bibliographic record
Abstract
Abstract Background PET imaging plays a critical role in the diagnosis and follow‐up of Alzheimer's disease (AD). However, current methods face significant limitations for two key measures: (1) Non‐displaceable binding potential (BP ND ) of amyloid‐beta (Aβ), which measures Aβ plaque accumulation but requires prolonged >100 min study times; (2) Cerebral blood flow (CBF), an indicator of neurodegeneration which typically requires an additional, separate study. To address these challenges, we developed a novel processing workflow which calculates absolute CBF from standard dynamic Aβ PET scans using our lab's flow‐modified 2‐tissue compartment model (F2TCM, EJNMMI Res.11:2 ), and estimates BP ND from a 20‐min dynamic scan post‐injection through multiple‐fold cross‐linear calibration . Method Data from 10 patients enrolled in the ongoing BioMind clinical trial data at our institution were used. Each dataset included a 45 min dynamic scan after injection of 300 MBq of 18F‐florbetaban and a 10 min scan at 110 min post‐injection, acquired with a GE Healthcare OMNI Legend PET/CT scanner. Images were reconstructed using the Q. Clear protocol for higher resolution arterial input function measurements, and the smoother VPHD protocol to improve signal‐to‐noise for CBF estimation. CBF was calculated using the F2TCM, BP ND using Logan graphical analysis relative to the cerebellum, and centiloid scores and SUVr were obtained with MIMneuro (MIM Software Inc.). Result Figure 1 shows the similarities/differences of CBF maps derived from 3‐, 20‐ and 45‐min dynamic scans for three patients with 18F‐florbetaben centiloids (SUVr) of 11.1 (1.03), 68.1 (1.38), and 164.4 (1.98), respectively. Voxel‐wise comparisons of CBF of all 10 patients showed mean MSE of 18±11 and 156±110 mL/min/100g for 20 and 3 min relative to 45 min, respectively. Figure 2 shows BP ND maps for a 45 min scan, followed by uncalibrated and calibrated 20 min scans. Calibration slopes, intercepts, uncalibrated and calibrated MSEs were 0.95±0.03, 0.08±0.01, 0.012±0.0044 and 0.0070±0.0013, respectively, based on five‐fold cross calibration. Conclusion This study highlights the potential of a streamlined 20‐minute Aβ PET imaging protocol to measure BP ND as a surrogate for the centiloid score, while also providing complementary CBF measurements to enhance diagnostic and prognostic utility.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.012 | 0.007 |
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".