Meeting Banner
Abstract #0672

Learning Systematic Imperfections and Image Reconstruction with Deep Neural Networks for Wave-Encoded Single-Shot Fast Spin Echo

Feiyu Chen1, Joseph Y Cheng2, Valentina Taviani3, John M Pauly1, and Shreyas S Vasanawala2

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States, 3GE Healthcare, Menlo Park, CA, United States

Wave-encoded single-shot fast spin echo imaging (SSFSE) achieves good structural delineation in less than a second while its calibration and reconstruction usually take more than a minute to finish. This study proposes a method to accelerate the calibration and reconstruction for wave-encoded SSFSE with a deep-learning-based approach. This method first learns the systematic imperfections with a deep neural network, and then reconstructs the image with another unrolled convolutional neural network. The proposed approach achieves 2.8-fold speedup compared with conventional approaches. Further, it can also reduce the ghosting and aliasing artifacts generated in conventional calibration and reconstruction approaches.

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

Join Here