Product-of-Gaussian-Mixture Diffusion Models for Joint Nonlinear MRI Reconstruction
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...
| Authors | Laurenz Nagler, Martin Zach, Thomas Pock |
|---|---|
| Primary category | cs.CV |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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