Language-enhanced generative modeling for amyloid PET synthesis from MRI and blood biomarkers.

Zhengjie Zhang, Xiaoxie Mao, Qihao Guo, Shaoting Zhang, Qi Huang, Mu Zhou, Fang Xie, Mianxin Liu

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Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Zhengjie ZhangShanghai Artificial Intelligence Laboratory, Shanghai 200232, China.

Xiaoxie MaoSchool of Medicine, Xiamen University, Xiamen, Fujian 361102, China.

Qihao GuoDepartment of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China.

Shaoting ZhangShanghai Artificial Intelligence Laboratory, Shanghai 200232, China.

Qi HuangDepartment of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China.

Mu ZhouDepartment of Computer Science, Rutgers University, Piscataway, NJ 08854, USA.

Fang XieDepartment of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China.

Mianxin LiuShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Assessment of amyloid pathology in Alzheimer's disease (AD) often relies on amyloid-beta positron emission tomography (Aβ-PET), but its clinical use is limited by cost and accessibility. We developed a language-enhanced generative framework to synthesize Aβ-PET images from T1-weighted magnetic resonance imaging (MRI) and blood biomarkers in a cohort of 566 participants. The synthetic PET images resembled real PET scans in both structural detail (structural similarity index [SSIM] = 0.920 ± 0.003) and regional uptake patterns (Pearson's R = 0.955 ± 0.007). In physician evaluation, diagnoses based on synthetic PET showed high agreement with those based on real PET (accuracy = 0.80). In addition, models using synthetic PET improved Aβ positivity classification performance compared with models based on MRI or blood biomarkers alone. These findings show that the framework can generate clinically informative PET-like images and may support resource-limited amyloid assessment workflows.

Indexed as

Alzheimer’s diseaseamyloid-beta PETblood-based biomarkerslarge language modelPET synthesis

Identifiers

PMID42620814

DOI10.1016/j.isci.2026.117122

PMCPMC13486886

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