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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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