将噪声模型融入扩散重建，提升MRI加速成像可靠性
Noisy MRI Reconstruction via MAP Estimation with an Implicit Deep-Denoiser Prior
- 基于MAP框架，用隐式去噪先验融合真实噪声模型
- 在模拟与真实数据上均优于现有深度学习与扩散方法
- 适合关注医学影像重建可解释性与实用性的研究者
加速磁共振成像（MRI）仍面临挑战，尤其在真实采集噪声条件下。尽管扩散模型近期在欠采样MRI重建中展现出潜力，但许多方法缺乏与底层MRI物理的显式关联，且对测量噪声敏感，限制了实际应用中的可靠性。我们提出隐式最大后验（ImMAP）框架，将采集噪声模型直接整合到最大后验（MAP）公式中。具体地，我们基于Kadkhodaie等人提出的随机上升法，并将其推广以处理MRI编码算子和真实测量噪声。在模拟与真实噪声数据集上，ImMAP始终优于当前最先进的深度学习方法（LPDSNet）和扩散方法（DDS）。通过揭示扩散模型在真实噪声条件下的实际表现与局限性，ImMAP建立了一个更可靠、更具可解释性的重建范式。
原文摘要 · Abstract (English)
Accelerating magnetic resonance imaging (MRI) remains challenging, particularly under realistic acquisition noise. While diffusion models have recently shown promise for reconstructing undersampled MRI data, many approaches lack an explicit link to the underlying MRI physics, and their parameters are sensitive to measurement noise, limiting their reliability in practice. We introduce Implicit-MAP (ImMAP), a diffusion-based reconstruction framework that integrates the acquisition noise model directly into a maximum a posteriori (MAP) formulation. Specifically, we build on the stochastic ascent method of Kadkhodaie et al. and generalize it to handle MRI encoding operators and realistic measurement noise. Across both simulated and real noisy datasets, ImMAP consistently outperforms state-of-the-art deep learning (LPDSNet) and diffusion-based (DDS) methods. By clarifying the practical behavior and limitations of diffusion models under realistic noise conditions, ImMAP establishes a more reliable and interpretable
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