论文标题
NPB-REC：基于深度学习的MRI重建的不确定性的非参数评估
NPB-REC: Non-parametric Assessment of Uncertainty in Deep-learning-based MRI Reconstruction from Undersampled Data
论文作者
论文摘要
基于深度学习（DL）图像重建模型中的不确定性定量对于基于重建图像的可靠临床决策至关重要。我们介绍了“ NPB-REC”，这是一个非参数完全贝叶斯的框架，用于从不足采样的“ K-Space”数据中进行MRI重建的不确定性评估。在训练阶段，我们使用随机梯度Langevin动力学（SGLD）来表征网络权重的后验分布。与基线E2E-VARNET相比，有和没有推理时间辍学的基线E2E-VARNET相比，我们从FastMRI挑战中证明了我们在多圈脑MRI数据集上的方法的附加价值。我们的实验表明，NPB-REC通过重建准确性优于基线（PSNR和SSIM $ 34.55 $，$ 0.908 $ vs. $ 33.08 $，$ 0.897 $，$ p <0.01 $）的高加速度率（$ r = 8 $）。这也在临床注释区域中进行了衡量。更重要的是，与蒙特 - 卡洛推理时间辍学方法相比，它提供了与重建误差相关的不确定性（Pearson相关系数$ r = 0.94 $ vs. $ r = 0.91 $）的不确定性。所提出的方法有可能促进从不足采样数据中安全利用基于DL的MRI重建方法。 \ url {https://github.com/samahkh/npb-rec}中可用代码和训练有素的模型。
Uncertainty quantification in deep-learning (DL) based image reconstruction models is critical for reliable clinical decision making based on the reconstructed images. We introduce "NPB-REC", a non-parametric fully Bayesian framework for uncertainty assessment in MRI reconstruction from undersampled "k-space" data. We use Stochastic gradient Langevin dynamics (SGLD) during the training phase to characterize the posterior distribution of the network weights. We demonstrated the added-value of our approach on the multi-coil brain MRI dataset, from the fastmri challenge, in comparison to the baseline E2E-VarNet with and without inference-time dropout. Our experiments show that NPB-REC outperforms the baseline by means of reconstruction accuracy (PSNR and SSIM of $34.55$, $0.908$ vs. $33.08$, $0.897$, $p<0.01$) in high acceleration rates ($R=8$). This is also measured in regions of clinical annotations. More significantly, it provides a more accurate estimate of the uncertainty that correlates with the reconstruction error, compared to the Monte-Carlo inference time Dropout method (Pearson correlation coefficient of $R=0.94$ vs. $R=0.91$). The proposed approach has the potential to facilitate safe utilization of DL based methods for MRI reconstruction from undersampled data. Code and trained models are available in \url{https://github.com/samahkh/NPB-REC}.
