Meeting Banner
Abstract #0465

Reconstruction of multi-shot diffusion-weighted MRI using unrolled network with U-nets as priors

Yuxin Hu1, Xinwei Shi1, Qiyuan Tian2, Hengkai Guo3, Minda Deng4, Miao Yu1, Catherine Moran5, Grant Yang1, Jennifer A McNab5, Bruce Daniel5,6, and Brian Hargreaves1,5,6

1Department of Electrical Engineering, Stanford University, Stanford, CA, United States, 2Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, 3ByteDance AI Lab, Beijing, China, 4Department of Applied Physics, Stanford University, Stanford, CA, United States, 5Department of Radiology, Stanford University, Stanford, CA, United States, 6Department of Bioengineering, Stanford University, Stanford, CA, United States

In this work, we demonstrated the feasibility of using deep neural networks for rapid multi-shot DWI reconstruction. An unrolled network with six U-nets, which operated in frequency and image domains alternatively, was shown to have the capability to remove aliasing artifacts from shot-to-shot phase variations, and achieved about 60-fold speed up and around 1% amplitude difference compared with conventional iterative reconstruction methods.

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

Join Here