Image Translation for Estimating Two‐Dimensional Axial Amyloid‐Beta PET From Structural MRI
Bibliographic record
Abstract
BACKGROUND: Amyloid-beta and brain atrophy are hallmarks for Alzheimer's Disease that can be targeted with positron emission tomography (PET) and MRI, respectively. MRI is cheaper, less-invasive, and more available than PET. There is a known relationship between amyloid-beta and brain atrophy, meaning PET images could be inferred from MRI. PURPOSE: To build an image translation model using a Conditional Generative Adversarial Network able to synthesize Amyloid-beta PET images from structural MRI. STUDY TYPE: Retrospective. POPULATION: Eight hundred eighty-two adults (348 males/534 females) with different stages of cognitive decline (control, mild cognitive impairment, moderate cognitive impairment, and severe cognitive impairment). Five hundred fifty-two subjects for model training and 331 for testing (80%:20%). FIELD STRENGTH/SEQUENCE: 3 T, T1-weighted structural (T1w). ASSESSMENT: The testing cohort was used to evaluate the performance of the model using the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR), comparing the likeness of the overall synthetic PET images created from structural MRI with the overall true PET images. SSIM was computed in the overall image to include the luminance, contrast, and structural similarity components. Experienced observers reviewed the images for quality, performance and tried to determine if they could tell the difference between real and synthetic images. STATISTICAL TESTS: greater than 0.96 in example images. From blinded readings, a Pearson Chi-squared test showed that there was no significant difference between the real and synthetic images by the observers (P = 0.68). RESULTS: A high degree of likeness across the evaluation set, which had a mean SSIM = 0.905 and PSNR = 2.685. The two observers were not able to determine the difference between the real and synthetic images, with accuracies of 54% and 46%, respectively. CONCLUSION: Amyloid-beta PET images can be synthesized from structural MRI with a high degree of similarity to the real PET images. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".