Abstract #0617

Improving Variable-Density Single-Shot Fast Spin Echo with Deep-Learning Reconstruction Using Variational Networks

Feiyu Chen¹, Valentina Taviani², Itzik Malkiel³, Joseph Y. Cheng⁴, Jamil Shaikh⁴, Stephanie Chang⁴, Christopher J. Hardy⁵, John M. Pauly¹, and Shreyas S. Vasanawala⁴

¹Electrical Engineering, Stanford University, Stanford, CA, United States, ²Global MR Applications and Workflow, GE Healthcare, Menlo Park, CA, United States, ³GE Global Research Centre, GE Healthcare, Herzliya, Israel, ⁴Radiology, Stanford University, Stanford, CA, United States, ⁵GE Global Research Centre, GE Healthcare, Niskayuna, NY, United States

In this work, a deep-learning-based reconstruction approach using a variational network (VN) was developed to accelerate the variable density single-shot fast spin echo (VD SSFSE) reconstruction. The image quality of this approach was clinically evaluated compared to standard parallel imaging and compressed sensing (PICS). The VN approach achieves improved image quality with higher perceived signal-to-noise ratio and sharpness. It also allows real-time image reconstruction of VD SSFSE sequences for practical clinical deployment.

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