University of California

Siemens Healthineers Ag

Chang Gao

Xiaodong Zhong

Peng Hu

Valid Ghodrati

John Paul Finn

A system and methods for recreating images from undersampled magnetic resonance imaging (MRI) image data to remove artifacts from the undersampling. Using trained transformer networks, undersampled k-space data can be acquired and the networks can predict additional projected radial MRI k-space data from the undersampled radial MRI k-space data. Images with fewer artifacts can then be generated.

Method and apparatus for accelerated acquisition and artifact reduction of undersampled MRI using a k-space transformer network

Systems and methods for generative adversarial networks (GANs) to remove artifacts from undersampled magnetic resonance (MR) images are described. The process of training the GAN can include providing undersampled 3D MR images to the generator model, providing the generated example and a real example to the discriminator model, applying adversarial loss, L2 loss, and structural similarity index measure loss to the generator model based on a classification output by the discriminator model, and repeating until the generator model has been trained to remove the artifacts from the undersampled 3D MR images. At runtime, the trained generator model of the GAN can be generate artifact-free images or parameter maps from undersampled MRI data of a patient.

Growing community of inventors

Los Angeles, CA, United States of America

Chang Gao