Xu Yonghong, Wang Pengfei, Ding Ling
Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2021 Apr 25;38(2):333-341. doi: 10.7507/1001-5515.202004043.
Diffusion tensor imaging technology can provide information on the white matter of the brain, which can be used to explore changes in brain tissue structure, but it lacks the specific description of the microstructure information of brain tissue. The neurite orientation dispersion and density imaging make up for its shortcomings. But in order to accurately estimate the brain microstructure, a large number of diffusion gradients are needed, and the calculation is complex and time-consuming through maximum likelihood fitting. Therefore, this paper proposes a kind of microstructure parameters estimation method based on the proximal gradient network, which further avoids the classic fitting paradigm. The method can accurately estimate the parameters while reducing the number of diffusion gradients, and achieve the purpose of imaging quality better than the neurite orientation dispersion and density imaging model and accelerated microstructure imaging via convex optimization model.
扩散张量成像技术能够提供有关大脑白质的信息，可用于探究脑组织结构的变化，但它缺乏对脑组织微观结构信息的具体描述。神经突方向离散度与密度成像弥补了其不足。然而，为了准确估计大脑微观结构，需要大量的扩散梯度，并且通过最大似然拟合进行计算既复杂又耗时。因此，本文提出了一种基于近端梯度网络的微观结构参数估计方法，该方法进一步避免了经典的拟合范式。该方法能够在减少扩散梯度数量的同时准确估计参数，并且实现比神经突方向离散度与密度成像模型以及通过凸优化模型的加速微观结构成像更好的成像质量目的。
