Amyloid‐Beta PET Synthesis from Structural MRI: A Potential Alternative Method for Alzheimer’s Disease Screening
Bibliographic record
Abstract
Abstract Background Amyloid‐beta and structural brain atrophy are known to be hallmarks of Alzheimer’s Disease (AD), and can be quantified with Positron Emission Tomography (PET) and structural Magnetic Resonance (MRI), respectively. PET scans use radiotracers that binds to amyloid‐beta molecules, whereas MRI measures changes in structural morphology. PET scans are difficult to perform due to cost (∼$5000/scan), invasiveness, and ionizing radiation exposure, making them inaccessible for screening early‐onset AD. Conversely, MRI is a cheaper (∼$500/scan), non‐invasive, and free from ionizing radiation technique, however, it cannot provide molecular information obtained from PET. There is a known relationship between amyloid‐beta and brain atrophy, hence, amyloid‐beta PET images might be synthesized from structural MRI using image translation, which is an advanced form of machine learning. Method An image translation algorithm was developed using the Open Access Series of Imaging Studies (OASIS‐3) dataset that provides 929 subjects with pairs of T1‐weighted MRI and amyloid‐beta PET images, where 609 are cognitively normal (CN) and 489 at different stages of cognitive decline based on Clinical Dementia Rating scores. The image translation algorithm implemented with Conditional Generative Adversarial Networks, shown in Figure 1, was trained with 550 pairs with 156 axial slices each of amyloid‐beta and MRI images. Eight models where generated with different parameters to assess their impact in the generalization quality, then compared in a sample of 334 subjects using Structural Similarity Index (SSIM) and Peak Signal‐to‐Noise Ratio (PSNR). Result Figure 2, compares the models performance based on SSIM and PSNR, where the best performing model generates synthetic PET images with a high degree of similarity with the real ones in Figure 3. Conclusion An image translation pipeline was implemented and explored by changing parameters that can affect the model performance, the developed models are robust against the different parameters that were changed and it demonstrates that amyloid‐beta PET images can be synthesized from structural MRI, reducing cost, invasiveness and increasing availability. Such a method could enable non‐invasive screening of early onset AD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".