Diffusion MRI (dMRI) is a widely used imaging modality, but requires long scanning times to acquire high resolution datasets. By leveraging the unique geometry present within this domain, we present a novel approach to dMRI angular super-resolution that extends upon the parametric continuous convolution (PCConv) framework. We introduce several additions to the operation including a Fourier feature mapping, global coordinates, and domain specific context. Using this framework, we build a fully parametric continuous convolution network (PCCNN) and compare against existing models. We demonstrate the PCCNN performs competitively while using significantly less parameters. Moreover, we show that this formulation generalises well to clinically relevant downstream analyses such as fixel-based analysis, and neurite orientation dispersion and density imaging.

翻译：扩散磁共振成像（dMRI）是一种广泛使用的成像模态，但获取高分辨率数据集需要较长的扫描时间。通过利用该领域内独特的几何结构，我们提出了一种新颖的dMRI角度超分辨率方法，该方法对参数化连续卷积（PCConv）框架进行了扩展。我们向该操作中引入了多项改进，包括傅里叶特征映射、全局坐标以及领域特定上下文。利用该框架，我们构建了一个全参数化连续卷积网络（PCCNN），并与现有模型进行了比较。我们证明PCCNN在使用显著更少参数的情况下仍能保持竞争力的性能。此外，我们展示了该公式能很好地泛化至临床相关的下游分析，如基于体素的分析以及神经突方向离散度与密度成像。