Keywords: AI Diffusion Models, Image Reconstruction
Motivation: Medical images in different modalities (MR\PET\CT) can provide different information, which can help to fully understand a patient's condition and assist physicians in making accurate diagnoses and treatment plans.
Goal(s): Using the diffusion model to generate multiple modal images from a single modality.
Approach: We propose the conditional latent diffusion model (CLDM) guided by category information to address the challenge of completing the target modal image within the same body.
Results: Compared to images generated by GANs, our model produces higher quality images with enhanced capabilities, particularly in capturing intricate details.
Impact: Our study offers bright future for diffusion models in the complementary field of medical imaging modalities.
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