DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
This work addresses accelerated MRI imaging for medical diagnostics, presenting an incremental improvement by integrating complex convolutions and data consistency into a deep learning framework.
The paper tackles fast parallel MRI reconstruction by proposing DeepcomplexMRI, a deep residual network with complex convolutions that uses multi-channel ground truth images for training and enforces k-space data consistency, achieving more accurate reconstructions compared to state-of-the-art methods.
This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network. Different from most existing works which rely on the utilization of the coil sensitivities or prior information of predefined transforms, DeepcomplexMRI takes advantage of the availability of a large number of existing multi-channel groudtruth images and uses them as labeled data to train the deep residual convolutional neural network offline. In particular, a complex convolutional network is proposed to take into account the correlation between the real and imaginary parts of MR images. In addition, the k space data consistency is further enforced repeatedly in between layers of the network. The evaluations on in vivo datasets show that the proposed method has the capability to recover the desired multi-channel images. Its comparison with state-of-the-art method also demonstrates that the proposed method can reconstruct the desired MR images more accurately.