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Abstract #4709

Accelerated MR image reconstruction using iterative feasible set projection

Doohee Lee1,2, Jaeyeon Yoon1,2, Jingyu Ko1,2, Jingu Lee1,2, Yoonho Nam3,4, and Jongho Lee1

1Seoul National University, Seoul, Korea, Republic of, 2AIRS medical, Seoul, Korea, Republic of, 3Department of Radiology, Seoul St. Mary’s Hospital, Seoul, Korea, Republic of, 4College of Medicine, The Catholic University of Korea, Seoul, Korea, Republic of

We proposed a new deep learning architecture for the reconstruction of highly undersampled data. The new architecture combines an iterative generative adversarial network (GAN) with a shared discriminator and interacts with data consistency blocks. The algorithm was applied to accelerate the data acquisition of the routine clinical protocols, particularly 2D Cartesian sampling sequences. The new method was tested to explore generalizability of the algorithm in in-vivo data under various conditions (difference pulse sequences, organs, coil types, sites, and health condition).

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