Dynamic Imaging Using a Deep Generative SToRM (Gen-SToRM) Model.

ABSTRACT: We introduce a generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The model assumes that the images in the dataset are non-linear mappings of low-dimensional latent vectors. We use the deep convolutional neural network (CNN) to represent the non-linear transformation. The parameters of the generator as well as the low-dimensional latent vectors are jointly estimated only from the undersampled measurements. This approach is different from traditional CNN approaches that require extensive fully sampled training data. We penalize the norm of the gradients of the non-linear mapping to constrain the manifold to be smooth, while temporal gradients of the latent vectors are penalized to obtain a smoothly va

SUBMITTER: Zou Q 

PROVIDER: S-EPMC8590205 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature