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Abstract #1962

Non-uniform Fast Fourier Transform via Deep Learning

Yuze Li1, Zhangxuan Hu2, Haikun Qi3, Guangqi Li1, Dongyue Si1, Haiyan Ding1, Hua Guo1, and Huijun Chen1
1Center for Biomedical Imaging Research, Medical School, Tsinghua University, Beijing, China, 2MR Research China, GE Healthcare, Beijing, China, 3King’s College London, London, United Kingdom

In this study, a deep learning-based MR reconstruction framework called DLNUFFT (Deep Learning-based Non-Uniform Fast Fourier Transform) was proposed, which can restore the under-sampled non-uniform k-space to fully sampled Cartesian k-space without NUFFT gridding. Novel network layers with fully learnable parameters were constructed to replace the hand-crafted convolution kernel and the density compensation in conventional NUFFT. Simulations and in-vivo results showed DLNUFFT can achieve higher performance than conventional NUFFT, compressed sensing and state-of-the-art deep learning methods in terms of PSNR and SSIM.

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