无需预标定线圈敏感度，直接从原始数据重建高质量MRI影像
Measurement Score-Based MRI Reconstruction with Automatic Coil Sensitivity Estimation
- 联合估计线圈敏感度与测量得分，实现自监督重建
- 在脑部fastMRI数据上逼近有清洁先验的扩散模型性能
- 适合无真实图像或线圈参数的临床重建场景
基于扩散的逆问题求解器（DIS）近期在压缩感知并行MRI重建中表现优异，通过结合扩散先验与物理测量模型实现高精度重建。然而，这类方法通常依赖预标定的线圈敏感度图（CSMs）和真实图像，实用性受限：重欠采样下准确估计CSMs困难，而真实图像往往不可得。本文提出无标定测量得分扩散模型（C-MSM），通过联合自动估计线圈敏感度与从k空间数据中自监督学习测量得分，消除对预标定CSMs和真实图像的依赖。C-MSM通过在部分测量后验得分上进行随机采样，近似完整后验分布，同时完成敏感度估计。在多线圈脑部fastMRI数据集上的实验表明，即使没有清洁训练数据和预标定的CSMs，C-MSM仍能达到接近使用干净扩散先验的DIS的重建性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。