Three-dimensional self-attention conditional GAN with spectral normalization for multimodal neuroimaging synthesis.

ABSTRACT:

Purpose

To develop a new 3D generative adversarial network that is designed and optimized for the application of multimodal 3D neuroimaging synthesis.

Methods

We present a 3D conditional generative adversarial network (GAN) that uses spectral normalization and feature matching to stabilize the training process and ensure optimization convergence (called SC-GAN). A self-attention module was also added to model the relationships between widely separated image voxels. The performance of the network was evaluated on the data set from ADNI-3, in which the proposed network was used to predict PET images, fractional anisotropy, and mean diffusivity maps from multimodal MRI. Then, SC-GAN was applied on a multidimensional diffusion MRI experiment for superresolution application. Expe

SUBMITTER: Lan H 

PROVIDER: S-EPMC9070032 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature