Image-and-Label Conditioning Latent Diffusion Model: Synthesizing A$\beta$-PET From MRI for Detecting Amyloid Status

Zaixin Ou, Yongsheng Pan, Fang Xie, Qihao Guo, Dinggang Shen

Abstract: Deposition of $\beta$-amyloid (A$\beta$), which is generally observed by A$\beta$-PET, is an important biomarker to evaluate subjects with early-onset dementia. However, acquisition of A$\beta$-PET usually suffers from high expense and radiation hazards, making A$\beta$-PET not commonly used as MRI. As A$\beta$-PET scans are only used to determine whether A$\beta$ deposition is positive or not, it is highly valuable to capture the underlying relationship between A$\beta$ deposition and other neuroimages (i.e., MRI) and detect amyloid status based on other neuroimages to reduce necessity of acquiring A$\beta$-PET. To this end, we propose an image-and-label conditioning latent diffusion model (IL-CLDM) to synthesize A$\beta$-PET scans from MRI scans by enhancing critical shared information to finally achieve MRI-based A$\beta$ 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$\beta$-PET scans (by IL-CLDM) can significantly help classification of A$\beta$ as positive or negative.

External IDs:doi:10.1109/jbhi.2024.3492020