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：本文提出一种建模扩散磁共振成像数据集的新方法，该方法仅利用单个被试数据即可受益于人脑结构连贯性。现有方法在单个体素内建模dMRI信号，忽视了体素间存在的连贯性。我们采用神经网络参数化球谐函数级数（NeSH），以角域和空间域连续的方式，表示人脑连接组计划数据集中单个被试的dMRI信号。使用该方法重建的dMRI信号呈现出更结构连贯的数据表征。梯度图像中的噪声被去除，纤维取向分布函数沿纤维束方向呈现平滑变化。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。本文还证明了在角域和空间域进行上采样可获得与现有方法相当甚至更优的重建效果。