Spill-in counts in the quantification of 18F-florbetapir on Aβ-negative subjects: the effect of including white matter in the reference region
Bibliographic record
Abstract
Abstract Background We aim to provide a systematic study of the impact of white matter (WM) spill-in on the calculation of standardized uptake value ratios (SUVRs) on Aβ-negative subjects, and we study the effect of including WM in the reference region as a compensation. In addition, different partial volume correction (PVC) methods are applied and evaluated. Methods We evaluated magnetic resonance imaging and18F-AV-45 positron emission tomography data from 122 cognitively normal (CN) patients recruited at the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Cortex SUVRs were obtained by using the cerebellar grey matter (CGM) (SUVRCGM) and the whole cerebellum (SUVRWC) as reference regions. The correlations between the different SUVRs and the WM uptake (WM-SUVRCGM) were studied in patients, and in a well-controlled framework based on Monte Carlo (MC) simulation. Activity maps for the MC simulation were derived from ADNI patients by using a voxel-wise iterative process (BrainViset). Ten WM uptakes covering the spectrum of WM values obtained from patient data were simulated for different patients. Three different PVC methods were tested (a) the regional voxel-based (RBV), (b) the iterative Yang (iY), and (c) a simplified analytical correction derived from our MC simulation. Results WM-SUVRCGMfollowed a normal distribution with an average of 1.79 and a standard deviation of 0.243 (13.6%). SUVRCGMwas linearly correlated to WM-SUVRCGM(r= 0.82, linear fit slope = 0.28). SUVRWCwas linearly correlated to WM-SUVRCGM(r= 0.64, linear fit slope = 0.13). Our MC results showed that these correlations are compatible with those produced by isolated spill-in effect (slopes of 0.23 and 0.11). The impact of the spill-in was mitigated by using PVC for SUVRCGM(slopes of 0.06 and 0.07 for iY and RBV), while SUVRWCshowed a negative correlation with SUVRCGMafter PVC. The proposed analytical correction also reduced the observed correlations when applied to patient data (r= 0.27 for SUVRCGM,r= 0.18 for SUVRWC). Conclusions There is a high correlation between WM uptake and the measured SUVR due to spill-in effect, and that this effect is reduced when including WM in the reference region. We also evaluated the performance of PVC, and we proposed an analytical correction that can be applied to preprocessed data.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".