Image-and-Label Conditioning Latent Diffusion Model: Synthesizing A β-PET From MRI for Detecting Amyloid Status - Ou, Zaixin; Pan, Yongsheng; Xie, Fang; Guo, Qihao; Shen, Dinggang (IEEE journal of biomedical and health informatics, 2025)
Deposition of β -amyloid (A β ), which is generally observed by A β -PET, is an important biomarker to evaluate subjects with early-onset dementia. However, acquisition of A β -PET usually suffers from high expense and radiation hazards, making A β -PET not commonly used as MRI. As A β -PET scans are only used to determine whether A β deposition is positive or not, it is highly valuable to capture the underlying relationship between A β deposition and other neuroimages (i.e., MRI) and detect amyloid status based on other neuroimages to reduce necessity of acquiring A β -PET. To this end, we propose an image-and-label conditioning latent diffusion model (IL-CLDM) to synthesize A β -PET scans from MRI scans by enhancing critical shared information to finally achieve MRI-based A β classification. Specifically, two conditioning modules are introduced to enable IL-CLDM to implicitly learn joint image synthesis and diagnosis: 1) an image conditioning module, to extract meaningful features from source MRI scans to provide structural information, and 2) a label conditioning module, to guide the alignment of generated scans to the diagnosed label. Experiments on a clinical dataset of 510 subjects demonstrate that our proposed IL-CLDM achieves image quality superior to five widely used models, and our synthesized A β -PET scans (by IL-CLDM) can significantly help classification of A β as positive or negative.