Yuxiang Dai1, Cheng yan Wang2, and He Wang1
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 2Human Phenome Institute, Fudan University, Shanghai, China
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 2Human Phenome Institute, Fudan University, Shanghai, China
We proposed a fusion model based on the optimization method to integrate the image and gradient-based priors into CS-MRI for better reconstruction results via convolutional neural network models. In addition, the proposed fusion model exhibited effective reconstruction performance in MRA.
Figure 1 The framework of the
proposed fusion model in which the above network is MDN and the below network is
SRLN. $$$N_f$$$ represents the number
of convolutional kernels, DF represents
the dilated factor of convolutional kernel. $$$c_n$$$ and $$$res_n$$$ represent n-th
convolution layer and residual learning respectively.
Figure 2 Reconstruction
results for 30% radial sampling. The first and third row include groundtruth
and reconstruction results of CSMRI methods. The second and fourth row include
radial sampling mask and errors. Values of PSNR and SSIM are shown in the upper
left corner.