Abstract
Deep learning has emerged as a promising approach for learning the nonlinear mapping between diffusion-weighted MR images and tissue parameters, which enables automatic and deep understanding of the brain microstructures. However, the efficiency and accuracy in the multi-parametric estimations are still limited since previous studies tend to estimate multi-parametric maps with dense sampling and isolated signal modeling. This paper proposes DeepMpMRI, a unified framework for fast and high-fidelity multi-parametric estimation from various diffusion models using sparsely sampled q-space data. DeepMpMRI is equipped with a newly designed tensor-decomposition-based regularizer to effectively capture fine details by exploiting the correlation across parameters. In addition, we introduce a Nesterov-based adaptive learning algorithm that optimizes the regularization parameter dynamically to enhance the performance. DeepMpMRI is an extendable framework capable of incorporating flexible network architecture. Experimental results demonstrate the superiority of our approach over 5 state-of-the-art methods in simultaneously estimating multi-parametric maps for various diffusion models with fine-grained details both quantitatively and qualitatively, achieving 4.5 - 22.5$\times$ acceleration compared to the dense sampling of a total of 270 diffusion gradients.
Abstract (translated)
深度学习已经成为了学习扩散加权磁共振图像（DWI）和组织参数之间非线性映射的有前途的方法，这使得我们能够自动和深入理解大脑微观结构。然而，多参数估计的效率和准确性仍然有限，因为以前的研究倾向于使用稀疏采样和离散信号建模来估计多参数映射。本文提出DeepMpMRI，一种基于稀疏采样q空间数据的统一框架，用于从各种扩散模型进行高速和高保真的多参数估计。DeepMpMRI配备了一个新设计的张量分解基于正则化的特征，通过利用参数之间的相关性有效地捕捉细节。此外，我们引入了一种Nesterov基于自适应学习算法，动态优化正则化参数以提高性能。DeepMpMRI是一个可扩展的框架，能够容纳灵活的网络架构。实验结果表明，我们的方法在同时估计多种扩散模型的细粒度多参数映射方面具有优越性，超过5种最先进的无监督学习方法，实现了4.5 - 22.5×的加速，相比总共270个扩散梯度的密集采样。
URL
https://arxiv.org/abs/2405.03159
