Odense-Oxford PET Image Analysis (OPETIA): An FSL-based toolbox for multimodal neuroimaging
Bibliographic record
Abstract
Advanced analysis of MRI and PET images provides quantitative and accurate information about the brain structure and function, allowing differential diagnosis, prognosis, and personalized treatment. Most clinical software lack accurate quantification. Here we developed a user-friendly multimodal neuroimage analysis toolbox, named Odense-Oxford PET Image Analysis (OPETIA), based on Functional Magnetic Resonance Imaging of the Brain Software Library (FSL) and Python programming language. FSL is a strong toolbox library for MRI analysis but has not been widely used for PET image analysis. OPETIA includes a graphical user interface that facilitates automatic multimodal neuroimage analysis. OPETIA can automatically pre-process magnetic resonance and PET images and calculates maximum, mean, and standard deviation of Standardized Uptake Value (SUV) and Standardized Uptake Value Ratio (SUVR) in the volumes of interest (VOI). To assess the efficacy of OPETIA, we analysed a set of static 18 F-fluorodeoxyglucose (FDG) PET and MRIs of healthy subjects and patients with Alzheimer’s disease (AD) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset using OPETIA and compared the SUVR measurements with those obtained from Statistical Parametric Mapping, version 12 (SPM12). The result of this comparison showed a close association between OPETIA and SPM12 results (p-value < 0.01, r > 0.8). OPETIA measurements were significantly (p-value < 0.01) larger than those of SPM12 in all brain regions (according to the Harvard-Oxford brain atlas), indicating a systematic difference between these tools. The Cronbach’s Alpha values for both tools were > 0.9, indicating a high reproducibility. We compared the group difference (control vs Alzheimer’s disease) obtained from each toolbox using two-sample t-test and found a significantly (p-value < 0.01) larger Cohen’s d values for SUVRs from OPETIA (d=0.22) than SPM12 (d=0.04). We suggest that OPETIA is a user-friendly and robust tool for quantitative analysis of multimodal neuroimaging such as cerebral PET and MR images.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.013 |
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".