mlsanityData to 7 Oct 2026

Language-Enhanced Generative Modeling for Amyloid PET Synthesis from MRI and Blood Biomarkers

arXiv 2511.02206 ↗First posted 4 Nov 2025cs.CVcited by 1

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Abstract (arXiv)

Background: Alzheimer's disease (AD) diagnosis heavily relies on amyloid-beta positron emission tomography (Abeta-PET), which is limited by high cost and limited accessibility. This study explores whether Abeta-PET spatial patterns can be predicted from blood-based biomarkers (BBMs) and MRI scans. Methods: We collected Abeta-PET images, T1-weighted MRI scans, and BBMs from 566 participants. A language-enhanced generative model, driven by a large language model (LLM) and multimodal information fusion, was developed to synthesize PET images. Synthesized images were evaluated for image quality, diagnostic consistency, and clinical applicability within a fully automated diagnostic pipeline. Findings: The synthetic PET images closely resemble real PET scans in both structural details (SSIM = 0.920 +/- 0.003) and regional patterns (Pearson's r = 0.955 +/- 0.007). Diagnostic outcomes using synthetic PET show high agreement with real PET-based diagnoses (accuracy = 0.80). Using synthetic PET, we developed a fully automatic AD diagnostic pipeline integrating PET synthesis and classification. The synthetic PET-based model (AUC = 0.78) outperforms T1-based (AUC = 0.68) and BBM-based (AUC = 0.73) models, while combining synthetic PET and BBMs further improved performance (AUC = 0.79). Ablation analysis supports the advantages of LLM integration and prompt engineering. Interpretation: Our language-enhanced generative model synthesizes realistic PET images, enhancing the utility of MRI and BBMs for Abeta spatial pattern assessment and improving the diagnostic workflow for Alzheimer's disease.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 79 references that are in the map. First posted on arXiv 4 Nov 2025.

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19 of 79 references.

References · 19

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7 more
  1. 2023A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnosticscited by 42
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  1. 2026Brain-WM: Brain Glioblastoma World Modelcited by 5