Synthesizing Aβ-Pet Via An Image And Label Conditioning Latent Diffusion Model For Detecting Amyloid Status

Deposition of β-amyloid is a crucial biomarker to evaluate subjects with early-onset dementia, often evaluated through Aβ-PET imaging. Aβ-PET is expensive and radiation-heavy; thus, it's advisable to avoid it unless medically necessary. Therefore there is a compelling need to classify Aβ and detect amyloid status using other neuroimaging modalities, capitalizing on the underlying relationship between different modalities. Here, we propose an image and label conditioning latent diffusion model to synthesize Aβ-PET scans for improving Aβ classification based on MRI and FDG-PET scans. We introduce two conditioning modules: (1) an image conditioning module to extract a meaningful feature map from two source modalities to provide structural and metabolism information for guidance, and (2) a label conditioning module to provide the specific guidance direction on image generation. Experiments on the clinical dataset demonstrate that our proposed method's synthetic Aβ-PET scans are reliable for classifying Aβ.