用贝叶斯方法同时重建MRI图像并量化不确定性，结果更准且可信赖。
Bayesian Uncertainty-Aware MRI Reconstruction
- 将MRI重建建模为贝叶斯逆问题，用总变差先验约束图像梯度稀疏性。
- 基于分裂增广吉布斯采样，实现后验分布高效采样，重建误差更低。
- 能有效生成不确定性图，与真实误差高度相关，适合临床可信度评估。
我们提出一种新框架，利用欠采样的k空间数据联合进行磁共振成像重建与不确定性量化。该问题被建模为贝叶斯线性逆问题，对未知模型参数赋予先验分布。具体地，假设目标图像在其空间梯度上稀疏，并施加总变差（Total Variation）先验。采用基于分裂增广吉布斯采样器的马尔可夫链蒙特卡洛（MCMC）方法，从未知参数的联合后验分布中进行采样。在单线圈和多线圈数据集上的实验表明，该框架在重建性能上优于基于优化的压缩感知算法。此外，本框架能有效量化不确定性，其输出的不确定性图与由重构图像与真实图像计算出的误差图具有强相关性。
原文摘要 · Abstract (English)
We propose a novel framework for joint magnetic resonance image reconstruction and uncertainty quantification using under-sampled k-space measurements. The problem is formulated as a Bayesian linear inverse problem, where prior distributions are assigned to the unknown model parameters. Specifically, we assume the target image is sparse in its spatial gradient and impose a total variation prior model. A Markov chain Monte Carlo (MCMC) method, based on a split-and-augmented Gibbs sampler, is then used to sample from the resulting joint posterior distribution of the unknown parameters. Experiments conducted using single- and multi-coil datasets demonstrate the superior performance of the proposed framework over optimisation-based compressed sensing algorithms. Additionally, our framework effectively quantifies uncertainty, showing strong correlation with error maps computed from reconstructed and ground-truth images.
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。