Image-and-Label Conditioning Latent Diffusion Model: Synthesizing Aβ-PET from MRI for Detecting Amyloid Status

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