Zero-shot Dynamic MRI Reconstruction with Global-to-local Diffusion Model

TL;DR

This paper introduces a zero-shot dynamic MRI reconstruction method using a global-to-local diffusion model that leverages merged data from adjacent frames, reducing the need for fully-sampled training data and maintaining high reconstruction quality.

Contribution

The paper presents a novel global-to-local diffusion framework for dynamic MRI reconstruction that enables zero-shot learning by merging data from neighboring frames.

Findings

Achieves noise reduction and detail preservation comparable to supervised methods.

Effectively reconstructs dynamic MRI with limited fully-sampled data.

Demonstrates robustness in handling spatio-temporal complexity.

Abstract

Diffusion models have recently demonstrated considerable advancement in the generation and reconstruction of magnetic resonance imaging (MRI) data. These models exhibit great potential in handling unsampled data and reducing noise, highlighting their promise as generative models. However, their application in dynamic MRI remains relatively underexplored. This is primarily due to the substantial amount of fully-sampled data typically required for training, which is difficult to obtain in dynamic MRI due to its spatio-temporal complexity and high acquisition costs. To address this challenge, we propose a dynamic MRI reconstruction method based on a time-interleaved acquisition scheme, termed the Glob-al-to-local Diffusion Model. Specifically, fully encoded full-resolution reference data are constructed by merging under-sampled k-space data from adjacent time frames, generating two distinct bulk training datasets for global and local models. The global-to-local diffusion framework alternately optimizes global information and local image details, enabling zero-shot reconstruction. Extensive experiments demonstrate that the proposed method performs well in terms of noise reduction and detail preservation, achieving reconstruction quality comparable to that of supervised approaches.