Meeting Banner
Abstract #4655

Dual-domain Generative Adversarial Model for Accelerated MRI Reconstruction

Guanhua Wang1,2, Enhao Gong2,3, Suchandrima Banerjee4, Karen Ying5, Greg Zaharchuk6, and John Pauly2

1Biomedical Engineering, Tsinghua University, Beijing, China, 2Electrical Engineering, Stanford University, Stanford, CA, United States, 3Subtle Medical, Menlo Park, CA, United States, 4Global Applied Science Laboratory, GE Healthcare, Menlo Park, CA, United States, 5Engineering Physics, Tsinghua University, Beijing, China, 6Radiology, Stanford University, Stanford, CA, United States

Previous CS frameworks based on Deep Learning like GANCS have demonstrated improved quality and efficiency. To further improve the restoration of the high-frequency details and the suppression of aliasing artifacts, a data-driven regularization is explicitly added on the k-space, in the form of an adversarial loss (GAN). In this work, the cross-domain generative adversarial model is trained and evaluated on diverse datasets and show decent generalization ability. For both quantitative comparison and visual inspection, the proposed method achieves better reconstruction than previous networks.

This abstract and the presentation materials are available to members only; a login is required.

Join Here