用混合高斯乘积扩散模型联合重建图像与线圈敏感度，提升灵活性和鲁棒性。
Product-of-Gaussian-Mixture Diffusion Models for Joint Nonlinear MRI Reconstruction
- 将图像先验与线圈敏感度平滑先验结合，联合建模重建过程
- 在不同对比度、解剖结构和采样轨迹下均表现稳定，速度快
- 新参数化增强图像先验表达能力，提升去噪与重建效果
近期扩散模型因生成图像质量高，被广泛用于磁共振成像重建。然而现有方法多依赖大型网络及不透明的时间条件机制，且需离线估计线圈敏感度，导致重建过程可解释性差，采集设置灵活性不足。为此，本文提出联合重建图像与线圈敏感度的方法：采用参数高效的混合高斯乘积扩散模型作为图像先验，结合线圈敏感度的经典光滑先验。该方法对对比度、解剖分布变化及不同k空间轨迹均表现出强鲁棒性，且计算高效。最后，我们设计了更丰富的图像先验参数化形式，在去噪与MRI重建任务中进一步提升了性能。
原文摘要 · Abstract (English)
Recently, diffusion models have attracted considerable attention for magnetic resonance image reconstruction due to their high sample quality. However, most existing methods rely on large networks with opaque time-conditioning mechanisms, and require offline coil sensitivity estimation. This results in limited interpretability of the reconstruction process and reduced flexibility in the acquisition setup. To address these limitations, we jointly reconstruct the image and the coil sensitivities by combining the parameter-efficient product-of-Gaussian-mixture diffusion model as an image prior with a classical smoothness prior on the coil sensitivities. The proposed method is fast and robust to both contrast and anatomical distribution shifts as well as changing k-space trajectories. Finally, we propose a more expressive parameterization of the image prior which improves results in denoising and magnetic resonance image reconstruction.
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