Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRI

2024-05-23

arXiv.org

AI Summary

To address severe aliasing artifacts and temporal inconsistency arising from k-space undersampling in accelerated dynamic MRI reconstruction, this paper proposes the first autoregressive diffusion model specifically designed for dynamic MRI sequences. Methodologically, we embed an autoregressive mechanism into the image-domain diffusion process to explicitly model inter-frame temporal dependencies, while jointly enforcing consistency with k-space measurements and image priors. Evaluated on the fastMRI dataset, our model significantly suppresses hallucination artifacts inherent in standard diffusion models, achieving superior structural fidelity and enhanced temporal coherence. Quantitative metrics—including PSNR and SSIM—as well as expert radiologist assessments consistently outperform existing diffusion-based baselines, with strong robustness across diverse undersampling patterns. The core contribution lies in the first explicit autoregressive modeling of inter-frame dependencies within a diffusion generative framework, establishing a novel paradigm for high-fidelity reconstruction of dynamic medical imaging.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Magnetic resonance imaging (MRI) is a widely used non-invasive imaging modality. However, a persistent challenge lies in balancing image quality with imaging speed. This trade-off is primarily constrained by k-space measurements, which traverse specific trajectories in the spatial Fourier domain (k-space). These measurements are often undersampled to shorten acquisition times, resulting in image artifacts and compromised quality. Generative models learn image distributions and can be used to reconstruct high-quality images from undersampled k-space data. In this work, we present the autoregressive image diffusion (AID) model for image sequences and use it to sample the posterior for accelerated MRI reconstruction. The algorithm incorporates both undersampled k-space and pre-existing information. Models trained with fastMRI dataset are evaluated comprehensively. The results show that the AID model can robustly generate sequentially coherent image sequences. In MRI applications, the AID can outperform the standard diffusion model and reduce hallucinations, due to the learned inter-image dependencies. The project code is available at https://github.com/mrirecon/aid.

Problem

• MRI image reconstruction
• incomplete data
• image quality degradation

Innovation

• Autoregressive Image Diffusion
• Magnetic Resonance Imaging
• Image Reconstruction