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.

该研究提出了在扩散磁共振成像中，使用参数连续卷积网络进行角度超分辨率，并且在全局坐标和领域特定上下文等方面进行了改进。该方法不仅可以有效降低参数数量，还能够泛化至临床相关的下游分析，如 Fixel 相关分析和神经元方向分散和密度成像。

扩散 MRI 中的超分辨率空间角度卷积

Spatio-Angular Convolutions for Super-resolution in Diffusion MRI

Standard convolutional neural networks assume a grid structured input is
available and exploit discrete convolutions as their fundamental building
blocks. This limits their applicability to many real-world applications. In
this paper we propose Parametric Continuous Convolution, a new learnable
operator that operates over non-grid structured data. The key idea is to
exploit parameterized kernel functions that span the full continuous vector
space. This generalization allows us to learn over arbitrary data structures as
long as their support relationship is computable. Our experiments show
significant improvement over the state-of-the-art in point cloud segmentation
of indoor and outdoor scenes, and lidar motion estimation of driving scenes.

本文提出了可操作非网格结构数据的一种新的可学习算子 —— 参数化连续卷积，并在室内外场景的点云分割和驾驶场景的激光雷达运动估计中实验证明，相较于现有技术有显著提升。