We propose a novel nonparametric model for diffusion MRI signal in q-space. In q-space, diffusion MRI signal is measured for a sequence of magnetic strengths (b-values) and magnetic gradient directions (b-vectors). We propose a Poly-RBF model, which employs a bidirectional framework with polynomial bases to model the signal along the b-value direction and Gaussian radial bases across the b-vectors. The model can accommodate sparse data on b-values and moderately dense data on b-vectors. We investigate the utility of Poly-RBF for two applications: 1) prediction of the dMRI signal, and 2) harmonization of dMRI data collected under different acquisition protocols with different scanners. The proposed Poly-RBF model can more accurately predict the unmeasured diffusion signal than its competitors such as the Gaussian process model in Eddy of FSL. Applying it to harmonizing the diffusion signal can significantly improve the reproducibility of derived white matter microstructure measures.

翻译：我们提出了一种用于q空间中扩散MRI信号的非参数模型。在q空间中，扩散MRI信号是针对一系列磁场强度（b值）和磁场梯度方向（b向量）进行测量的。我们提出了Poly-RBF模型，该模型采用双向框架，利用多项式基对b值方向的信号进行建模，并通过高斯径向基函数对b向量方向进行建模。该模型能够适应b值上的稀疏数据和b向量上的中等密度数据。我们研究了Poly-RBF在两项应用中的效用：1) 预测dMRI信号，2) 协调不同扫描仪、不同采集协议下收集的dMRI数据。相较于其竞争对手（如FSL中Eddy模块的高斯过程模型），所提出的Poly-RBF模型能够更准确地预测未测量的扩散信号。将其应用于扩散信号的协调，可以显著提高衍生白质微结构测量的可重复性。