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 Daniel^5,6, and Brian Hargreaves^1,5,6

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.

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