标题： 基于测量评分的MRI重建与自动线圈灵敏度估计 显示英文标题

标题： Measurement Score-Based MRI Reconstruction with Automatic Coil Sensitivity Estimation

摘要： 基于扩散的逆问题求解器（DIS）最近在结合扩散先验与物理测量模型的压缩感知并行MRI重建中表现出色。 然而，它们通常依赖于预先校准的线圈敏感度图（CSMs）和真实图像，这使得它们在实际应用中往往不切实际：在严重欠采样下难以准确估计CSMs，且真实图像通常不可用。 我们提出了无校准的测量得分扩散模型（C-MSM），这是一种新方法，通过直接从k空间数据中联合执行自动CSM估计和自我监督的测量得分学习，消除了这些依赖性。 C-MSM通过在部分测量后验得分上进行随机采样来近似完整后验分布以重建图像，同时估计CSMs。 在多线圈脑部fastMRI数据集上的实验表明，C-MSM在没有清洁训练数据和预先校准的CSMs的情况下，实现了接近使用清洁扩散先验的DIS的重建性能。 显示英文摘要

摘要： Diffusion-based inverse problem solvers (DIS) have recently shown outstanding performance in compressed-sensing parallel MRI reconstruction by combining diffusion priors with physical measurement models. However, they typically rely on pre-calibrated coil sensitivity maps (CSMs) and ground truth images, making them often impractical: CSMs are difficult to estimate accurately under heavy undersampling and ground-truth images are often unavailable. We propose Calibration-free Measurement Score-based diffusion Model (C-MSM), a new method that eliminates these dependencies by jointly performing automatic CSM estimation and self-supervised learning of measurement scores directly from k-space data. C-MSM reconstructs images by approximating the full posterior distribution through stochastic sampling over partial measurement posterior scores, while simultaneously estimating CSMs. Experiments on the multi-coil brain fastMRI dataset show that C-MSM achieves reconstruction performance close to DIS with clean diffusion priors -- even without access to clean training data and pre-calibrated CSMs.