用分阶段扩散模型同时修复运动伪影并重建磁共振图像。
Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Models
- 分阶段去噪策略先处理大尺度运动，再重建低频图像。
- 在64倍欠采样下仍能有效恢复心脏动态MRI的运动伪影。
- 不依赖采样模式和扫描协议，适合多种临床场景。
磁共振成像（MRI）因需长时间采集k空间数据，极易受运动伪影影响，尤其在动态成像中会降低诊断价值。本文提出一种新颖的交替优化框架，利用定制化的扩散模型联合重建与校正非刚性运动造成的k空间数据失真。该扩散模型采用粗到细的去噪策略，先捕捉整体运动趋势，再重建图像低频成分，为运动估计提供优于标准扩散模型的归纳偏置。我们在真实心脏电影MRI数据集以及复杂模拟的刚性与非刚性形变上验证了方法性能，即使每个运动状态被64倍欠采样，也能有效恢复。此外，该方法对采样模式、解剖差异和扫描协议均具有鲁棒性，仅要求每个运动状态中至少包含部分低频信息。
原文摘要 · Abstract (English)
Magnetic Resonance Imaging (MRI) is highly susceptible to motion artifacts due to the extended acquisition times required for k-space sampling. These artifacts can compromise diagnostic utility, particularly for dynamic imaging. We propose a novel alternating minimization framework that leverages a bespoke diffusion model to jointly reconstruct and correct non-rigid motion-corrupted k-space data. The diffusion model uses a coarse-to-fine denoising strategy to capture large overall motion and reconstruct the lower frequencies of the image first, providing a better inductive bias for motion estimation than that of standard diffusion models. We demonstrate the performance of our approach on both real-world cine cardiac MRI datasets and complex simulated rigid and non-rigid deformations, even when each motion state is undersampled by a factor of 64x. Additionally, our method is agnostic to sampling patterns, anatomical variations, and MRI scanning protocols, as long as some low frequency components are sampled during each motion state.
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