We present a novel way to model diffusion magnetic resonance imaging (dMRI) datasets, that benefits from the structural coherence of the human brain while only using data from a single subject. Current methods model the dMRI signal in individual voxels, disregarding the intervoxel coherence that is present. We use a neural network to parameterize a spherical harmonics series (NeSH) to represent the dMRI signal of a single subject from the Human Connectome Project dataset, continuous in both the angular and spatial domain. The reconstructed dMRI signal using this method shows a more structurally coherent representation of the data. Noise in gradient images is removed and the fiber orientation distribution functions show a smooth change in direction along a fiber tract. We showcase how the reconstruction can be used to calculate mean diffusivity, fractional anisotropy, and total apparent fiber density. These results can be achieved with a single model architecture, tuning only one hyperparameter. In this paper we also demonstrate how upsampling in both the angular and spatial domain yields reconstructions that are on par or better than existing methods.

翻译：我们提出一种新型扩散磁共振成像（dMRI）数据集建模方法，该方法仅利用单一位于人类大脑结构连贯性优势的数据对象。现有方法对个体体素的dMRI信号独立建模，忽视了实际存在的体素间连贯性。我们采用神经网络参数化球谐函数级数（NeSH），用于表征人类连接组计划数据集中单个对象的dMRI信号，该表征在角域和空间域均保持连续性。该方法重建的dMRI信号呈现出更具结构连贯性的数据表征：梯度图像中的噪声得到消除，纤维取向分布函数沿纤维束呈现平滑的方向变化。我们展示了如何利用该重建结果计算平均扩散率、分数各向异性和总表观纤维密度。这些结果可通过单一模型架构实现，仅需调节一个超参数。本文同时证实，在角域和空间域进行上采样所获得的重建结果，均达到或超越现有方法的性能水平。