DELIMIT PyTorch - An extension for Deep Learning in Diffusion Imaging
This work addresses a domain-specific challenge in medical imaging by providing tools for deep learning on spherical diffusion data, representing an incremental advancement in specialized frameworks.
The authors tackled the problem of applying deep learning to spherical diffusion imaging data by developing DELIMIT, a PyTorch extension that introduces novel layers for spherical harmonic interpolation and local spherical convolution, enabling convenient processing and preprocessing of such signals.
DELIMIT is a framework extension for deep learning in diffusion imaging, which extends the basic framework PyTorch towards spherical signals. Based on several novel layers, deep learning can be applied to spherical diffusion imaging data in a very convenient way. First, two spherical harmonic interpolation layers are added to the extension, which allow to transform the signal from spherical surface space into the spherical harmonic space, and vice versa. In addition, a local spherical convolution layer is introduced that adds the possibility to include gradient neighborhood information within the network. Furthermore, these extensions can also be utilized for the preprocessing of diffusion signals.