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

2026-02-20

AI Summary

This work proposes CoPeDiT, a general-purpose 3D MRI generative model designed to address prevalent clinical data incompleteness issues such as missing modalities in multimodal brain MRI and absent slices in cardiac MRI. CoPeDiT introduces a novel integrity-aware mechanism embedded directly within the generative process, eliminating the need for external explicit masks. Its CoPeVAE encoder intrinsically perceives missing data patterns and generates semantic prompts that guide synthesis. Coupled with MDiT3D—a diffusion Transformer architecture specifically tailored for 3D MRI—CoPeDiT achieves high-fidelity image generation with consistent anatomical and pathological details. Extensive experiments on three large-scale MRI datasets demonstrate that CoPeDiT significantly outperforms existing methods in robustness, generalization, and flexibility.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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. Existing methods rely on external guidance to supply detailed missing state for instructing generative models to synthesize missing MRIs. However, manual indicators are not always available or reliable in real-world scenarios due to the unpredictable nature of clinical environments. Moreover, these explicit masks are not informative enough to provide guidance for improving semantic consistency. In this work, we argue that generative models should infer and recognize missing states in a self-perceptive manner, enabling them to better capture subtle anatomical and pathological variations. Towards this goal, we propose CoPeDiT, a general-purpose latent diffusion model equipped with completeness perception for unified synthesis of 3D MRIs. Specifically, we incorporate dedicated pretext tasks into our tokenizer, CoPeVAE, empowering it to learn completeness-aware discriminative prompts, and design MDiT3D, a specialized diffusion transformer architecture for 3D MRI synthesis, that effectively uses the learned prompts as guidance to enhance semantic consistency in 3D space. Comprehensive evaluations on three large-scale MRI datasets demonstrate that CoPeDiT significantly outperforms state-of-the-art methods, achieving superior robustness, generalizability, and flexibility. The code is available at https://github.com/JK-Liu7/CoPeDiT .

Problem

• missing data
• MRI synthesis
• semantic consistency
• multi-modal MRI
• 3D medical imaging

Innovation

• completeness perception
• latent diffusion model
• 3D MRI synthesis
• self-perceptive generation
• diffusion transformer