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
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
A deep learning-based MR reconstruction framework called DLNUFFT
was proposed, which can restore the under-sampled non-uniform k-space to fully
sampled Cartesian k-space without NUFFT gridding. Results
showed DLNUFFT can achieve higher performance than compared methods.
Figure 1. The network structure of
DLNUFFT. (a) Under-sampled k-space data Xu is processed by Block Layer to obtain Xp with multiple patches. Xp goes through the Density Compensation
and Reordering Layer, which is initialized using the position map. After
Adaptive Interpolation Layer, the fully sampled Cartesian patch data Xkf are obtained, followed by ReBlock Layer to
generate the fully sampled k-space data Xf.
Finally, the inverse FFT is applied on Xf to get the reconstructed image If. (b) Illustration of real
acquired k-space data processed by DLNUFFT and the output after each block.
Figure 4. Reconstruction results for
DLNUFFT and other methods on three datasets with radial trajectory (R=4). DLNUFFT can achieve relatively high
PSNR (36.65dB) and the highest SSIM (93.21%).