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提供脑白质信息，可用于探究脑区组织结构变化，但缺乏对脑组织微结构信息的特异性描述。神经突起方向离散度与密度成像模型弥补了其不足，但获取脑组织微结构的准确估计参数需要大量的扩散梯度，同时通过最大似然拟合，整个过程计算复杂、消耗时间长。为此本文提出了一种基于近端梯度网络估计微结构参数的方法，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情况下，仍能够准确估计参数，实现成像质量优于神经突起方向离散度与密度成像模型和通过凸优化加速微观结构成像模型的目的。.
Keywords: diffusion magnetic resonance; neural network; neurite orientation dispersion and density imaging; tissue microstructure.