arXiv Computer Vision By Junkai Liu, Nay Aung, Theodoros N. Arvanitis, Joao A. C. Lima, Steffen E. Petersen, Le Zhang

Exploiting Completeness Perception with Diffusion Transformer for Unified 3D MRI Synthesis

Read the original on arXiv Computer Vision →

arXiv:2602. 18400v3 Announce Type: replace-cross Abstract: Missing data problems, such as missing modalities in multi-modal brain MRI and missing slices in cardiac MRI, pose significant challenges in clinical practice.

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arXiv AI
4d ago

Multimodal synthesis of MRI and tabular data with diffusion in a joint latent space via cross-attention

The paper introduces a multimodal latent diffusion model that jointly synthesizes volumetric MRI scans and tabular clinical data within a shared latent space using cross‑attention. By fusing the modalities through a variational autoencoder before diffusion, the model reconstructs MRI and tabular data with separate decoders, producing anatomically plausible images that align with synthesized clinical attributes. Evaluation on the German National Cohort dataset shows high‑fidelity image generation and superior tabular performance compared to CTGAN, marking the first demonstration of joint MRI‑tabular modeling in a single diffusion framework.

By Daniel Mensing, Jan Kapar, Jochen G. Hirsch, Matthias G\"unther, Horst Hahn, Marvin N. Wright