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Abstract #3482

PROJECTION GAN: Highly accelerated projection reconstruction using generative adversarial neural network

Tetiana Dadakova1, Jian Wu1, Hyun-Kyung Chung1, Brian Anhalt1, Dmitry Tkach1, Alexander Graff1, Natalie Marie Schenker-Ahmed1, David Karow1, and Christine Leon Swisher1
1Human Longevity, Inc., San Diego, CA, United States

Many clinical MRI applications in chest and abdomen require low sensitivity to motion. In addition, high acquisition speed is necessary for imaging in non-cooperative patients or those unable to perform breath holds. These applications would benefit from the highly accelerated radial acquisition. Deep learning has been shown to provide good results for image reconstruction from highly under-sampled k-space data. Here we introduce a Projection GAN - a generative adversarial neural network, which is trained to reconstruct highly accelerated MR images from uniformly rotated projections. Our results show that even with aggressive under-sampling the reconstruction has great overall performance.

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