EPC-3D-Diff: Equivariant Physics Consistent Conditional 3D Latent Diffusion for CBCT to CT Synthesis
Researchers have developed EPC-3D-Diff, a new conditional 3D latent diffusion model designed to improve the synthesis of CT images from CBCT data. This model incorporates a physics-derived equivariance loss that ensures consistency between the synthesized 3D volumes and their corresponding 2D projections. By performing diffusion in a compressed latent space, EPC-3D-Diff achieves efficient and stable training, leading to significant improvements in image quality metrics like PSNR and SSIM, as well as enhanced HU accuracy for radiotherapy applications. AI
IMPACT Improves medical image synthesis for radiotherapy, potentially leading to more accurate treatment planning.