Unified Multi-plane Autoregressive Diffusion for 3D Multi-Contrast MRI Synthesis

Acquiring a complete set of magnetic resonance imaging (MRI)contrasts is time-intensive and uncomfortable for patients, despite thediagnostic value of multi-contrast imaging. This motivates synthesizingmissing contrasts from those already acquired, which is an inherently3D problem requiring anatomical coherence across axial, sagittal, andcoronal planes. However, fully 3D generative models are often impracti-cal under computational resources that scale cubically with volume size.We propose a unified Multi-Plane Autoregressive Diffusion (MPAD), alatent diffusion framework that achieves full-volume 3D synthesis usingefficient plane-wise 2D operations while preserving volumetric coherence.A 3D autoencoder first compresses MRI scans into an isotropic 3D la-tent representation. A 2D diffusion model is then trained to reconstructmasked latent slices of the target contrast, conditioned on both source-contrast slices and unmasked target-contrast slices. During inference,we introduce plane-wise autoregressive synthesis with inter-plane priors.Slices are generated autoregressively in random order within one planeorientation to maintain intra-plane continuity, then propagated as con-ditioning priors to orthogonal plane orientations to enforce inter-planeconsistency. Compared to 3D latent diffusion baselines, MPAD reducestraining and inference FLOPs by 7× and 3×, respectively, while alsolowering inference time and peak memory consumption. Experimentson multiple datasets demonstrate that MPAD achieves superior perfor-mance, generating high-fidelity 3D volumes and supporting one-to-manytranslation within a single unified model.