Yan Lu

The authors propose MPAD: a 3D autoencoder compresses MRI volumes into an isotropic latent representation, a 2D diffusion model reconstructs masked target-contrast latent slices conditioned on source-contrast slices, unmasked target slices, and a text modality prompt, and inference generates slices autoregressively per plane while propagating completed volumes as priors to orthogonal planes, achieving full-volume 3D synthesis with 2D operations, reducing training and inference FLOPs by 7x and 3x versus 3D latent diffusion baselines, and improving PSNR, SSIM, and NMSE on ADNI and IXI.

The authors propose MPAD: a 3D autoencoder compresses MRI volumes into an isotropic latent representation, a 2D diffusion model reconstructs masked target-contrast latent slices conditioned on source-contrast slices, unmasked target slices, and a text modality prompt, and inference generates slices autoregressively per plane while propagating completed volumes as priors to orthogonal planes, achieving full-volume 3D synthesis with 2D operations, reducing training and inference FLOPs by 7x and 3x versus 3D latent diffusion baselines, and improving PSNR, SSIM, and NMSE on ADNI and IXI.