School of Information and Electronics, Beijing Institute of Technology, Room 316, Building 4, 5 Zhongguancun South Street, Beijing 100081, China.
School of Information and Electronics, Beijing Institute of Technology, Room 316, Building 4, 5 Zhongguancun South Street, Beijing 100081, China.
Med Image Anal. 2020 Apr;61:101650. doi: 10.1016/j.media.2020.101650. Epub 2020 Jan 22.
Deep learning based methods have improved the estimation of tissue microstructure from diffusion magnetic resonance imaging (dMRI) scans acquired with a reduced number of diffusion gradients. These methods learn the mapping from diffusion signals in a voxel or patch to tissue microstructure measures. In particular, it is beneficial to exploit the sparsity of diffusion signals jointly in the spatial and angular domains, and the deep network can be designed by unfolding iterative processes that adaptively incorporate historical information for sparse reconstruction. However, the number of network parameters is huge in such a network design, which could increase the difficulty of network training and limit the estimation performance. In addition, existing deep learning based approaches to tissue microstructure estimation do not provide the important information about the uncertainty of estimates. In this work, we continue the exploration of tissue microstructure estimation using a deep network and seek to address these limitations. First, we explore the sparse representation of diffusion signals with a separable spatial-angular dictionary and design an improved deep network for tissue microstructure estimation. The procedure for updating the sparse code associated with the separable dictionary is derived and unfolded to construct the deep network. Second, with the formulation of sparse representation of diffusion signals, we propose to quantify the uncertainty of network outputs with a residual bootstrap strategy. Specifically, because of the sparsity constraint in the signal representation, we perform a Lasso bootstrap strategy for uncertainty quantification. Experiments were performed on brain dMRI scans with a reduced number of diffusion gradients, where the proposed method was applied to two representative biophysical models for describing tissue microstructure and compared with state-of-the-art methods of tissue microstructure estimation. The results show that our approach compares favorably with the competing methods in terms of estimation accuracy. In addition, the uncertainty measures provided by our method correlate with estimation errors and produce reasonable confidence intervals; these results suggest potential application of the proposed uncertainty quantification method in brain studies.
基于深度学习的方法通过减少扩散梯度的数量提高了从扩散磁共振成像（dMRI）扫描中估算组织微观结构的能力。这些方法学习了从体素或斑块中的扩散信号到组织微观结构测量的映射。特别是，联合利用扩散信号在空间和角度域中的稀疏性是有益的，并且可以通过展开自适应地合并历史信息以进行稀疏重建的迭代过程来设计深度网络。然而，在这种网络设计中，网络参数的数量是巨大的，这可能会增加网络训练的难度并限制估计性能。此外，现有的基于深度学习的组织微观结构估计方法并没有提供关于估计不确定性的重要信息。在这项工作中，我们继续探索使用深度网络进行组织微观结构估计，并寻求解决这些限制。首先，我们探索了扩散信号的可分离空间-角度字典稀疏表示，并设计了用于组织微观结构估计的改进深度网络。推导并展开了与可分离字典相关的稀疏码更新过程，以构建深度网络。其次，基于扩散信号稀疏表示的公式，我们提出使用残差引导策略来量化网络输出的不确定性。具体来说，由于信号表示中的稀疏性约束，我们执行了用于不确定性量化的 Lasso 引导策略。在具有较少扩散梯度的脑 dMRI 扫描上进行了实验，其中将所提出的方法应用于两种用于描述组织微观结构的代表性生物物理模型，并与最新的组织微观结构估计方法进行了比较。结果表明，我们的方法在估计精度方面优于竞争方法。此外，我们方法提供的不确定性度量与估计误差相关，并产生合理的置信区间；这些结果表明所提出的不确定性量化方法在脑研究中有潜在的应用。
